U.S. patent application number 17/509784 was filed with the patent office on 2022-05-19 for mobile device image item replacements.
The applicant listed for this patent is Houzz, Inc.. Invention is credited to Xin Ai, Xiaoyi Huang, Jingwen Wang, Yi Wu.
Application Number | 20220157028 17/509784 |
Document ID | / |
Family ID | 1000006114074 |
Filed Date | 2022-05-19 |
United States Patent
Application |
20220157028 |
Kind Code |
A1 |
Huang; Xiaoyi ; et
al. |
May 19, 2022 |
MOBILE DEVICE IMAGE ITEM REPLACEMENTS
Abstract
A system for replacing physical items in images is discussed. A
depicted item can be selected and removed from an image via image
mask data and pixel merging techniques. Virtual light source
positions can be generated based on real-world light source data
from the image. A rendered simulation of a virtual item can then be
integrated into the image to create a modified image for
display.
Inventors: |
Huang; Xiaoyi; (Palo Alto,
CA) ; Wang; Jingwen; (Palo Alto, CA) ; Wu;
Yi; (San Jose, CA) ; Ai; Xin; (Palo Alto,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Houzz, Inc. |
Palo Alto |
CA |
US |
|
|
Family ID: |
1000006114074 |
Appl. No.: |
17/509784 |
Filed: |
October 25, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16521359 |
Jul 24, 2019 |
11164384 |
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17509784 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 19/006 20130101;
G06T 2200/24 20130101; G06F 3/0482 20130101; G06F 3/04883 20130101;
G06T 15/506 20130101 |
International
Class: |
G06T 19/00 20060101
G06T019/00; G06F 3/04883 20060101 G06F003/04883; G06T 15/50
20060101 G06T015/50; G06F 3/0482 20060101 G06F003/0482 |
Claims
1.-20. (canceled)
21. A method comprising: generating, using one or more processors
of a user device, an image of a physical environment; receiving, on
a display device of the user device, a selection of an object to be
replaced in the image; determining a three-dimensional orientation
of the object as depicted within the image using a pose detection
neural network comprising a convolutional neural network trained to
detect three-dimensional orientation of objects in a plurality of
object training images, the objects of the plurality of object
training images being of a same type as the object detected in the
image; removing, from the image, the object using regions that are
proximate to the object in the image; and generating a modified
image that depicts a render of a virtual model that replaces the
object in the physical environment.
22. The method of claim 21, further comprising: generating the
render of the virtual model in the three-dimensional orientation
and as illuminated by one or more virtual light sources based on a
lighting scheme in the image.
23. The method of claim 22, further comprising: determining the
lighting scheme of the image.
24. The method of claim 23, wherein determining the lighting scheme
comprises determining one or more bright regions of the image.
25. The method of claim 24, further comprising: positioning, in a
virtual environment, the one or more virtual light sources based on
locations of the one or more bright regions of the image.
26. The method of claim 24, wherein the determining of the one or
more bright regions of the image comprises determining an area of
pixels in the image having higher brightness values.
27. The method of claim 21, wherein, in the image, the object is
depicted in an object image region, and the regions that are
proximate to the object in the image are proximate regions that are
external to the object image region.
28. The method of claim 27, wherein the object is removed by
merging the proximate regions and the object image region.
29. The method of claim 28, wherein the proximate regions and the
object image region are merged using a neural network that
implements partial convolutional layers.
30. The method of claim 27, wherein the object is removed by
interpolating the proximate regions and the object image
region.
31. The method of claim 21, further comprising: displaying the
image on a display device of the user device; and receiving
selection of the object through the display device of the user
device.
32. The method of claim 31, wherein receiving selection of the
object comprises receiving selection of a selected region of the
image that depicts the object.
33. The method of claim 32, further comprising: generating an image
mask using the selected region.
34. The method of claim 32, further comprising: segmenting the
image into segment regions using an image segmentation
convolutional neural network (CNN), wherein the selected region is
identified from a user input on the image as displayed on the
display device of the user device.
35. The method of claim 34, wherein the user input is one of: a tap
gesture or a click.
36. The method of claim 32, wherein receiving selection of the
object through the display device comprises: receiving, on the
display device of the user device, a swipe gesture over at least a
portion of the object as depicted in the image.
37. A system comprising: one or more processors; a display device a
memory storing instructions that, when executed by the one or more
processors, cause the system to perform operations comprising:
generating an image of a physical environment; receiving, on the
display device, a selection of an object to be replaced in the
image; determining a three-dimensional orientation of the object as
depicted within the image using a pose detection neural network
comprising a convolutional neural network trained to detect
three-dimensional orientation of objects in a plurality of object
training images, the objects of the plurality of object training
images being of a same type as the object detected in the image;
removing, from the image, the object using regions that are
proximate to the object in the image; and generating a modified
image that depicts a render of a virtual model that replaces the
object in the physical environment.
38. The system of claim 37, the operations further comprising:
generating the render of the virtual model in the three-dimensional
orientation and as illuminated by one or more virtual light sources
based on a lighting scheme in the image.
39. The system of claim 38, the operations further comprising:
determining the lighting scheme of the image.
40. A machine-readable storage device embodying instructions that,
when executed by a device, cause the device to perform operations
comprising: generating an image of a physical environment;
receiving, on a display device, a selection of an object to be
replaced in the image; determining a three-dimensional orientation
of the object as depicted within the image using a pose detection
neural network comprising a convolutional neural network trained to
detect three-dimensional orientation of objects in a plurality of
object training images, the objects of the plurality of object
training images being of a same type as the object detected in the
image; removing, from the image, the object using regions that are
proximate to the object in the image; and generating a modified
image that depicts a render of a virtual model that replaces the
object in the physical environment.
Description
PRIORITY APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 16/521,359, filed. Jul. 24, 2019, the content
of which is incorporated herein by reference in its entirety.
TECHNICAL FIELD
[0002] Embodiments of the present disclosure relate generally to
image manipulation and, more particularly, but not by way of
limitation, to image processing.
BACKGROUND
[0003] Increasingly, users would like to simulate an object (e.g.,
chair, table, lamp) in a physical room without having access to the
object. For example, a user may be browsing a network site (e.g.,
website) and see a floor lamp that may or may not match the style
of the user's living room. The user may take a picture of his
living room and overlay an image of the floor lamp in the picture
to simulate what the floor lamp would look like in the living room.
However, it can be difficult to adjust the floor lamp within the
modeling environment using a mobile client device, which has
limited resources (e.g., a small screen, limited processing power).
Additionally, if the user living room already has a floor lamp, it
is difficult to replace the physical floor lamp in the image with a
simulated floor lamp through the mobile client device (e.g., in
images or video generated by the mobile client device).
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] To easily identify the discussion of any particular element
or act, the most significant digit or digits in a reference number
refer to the figure ("FIG.") number in which that element or act is
first introduced.
[0005] FIG. 1 is a block diagram showing an example network
architecture for physical item replacement and simulations,
according to some example embodiments.
[0006] FIG. 2 shows example internal functional engines of a
physical item replacement system, according to some example
embodiments.
[0007] FIG. 3 shows a flow diagram of an example method for
physical item replacement, according to some example
embodiments.
[0008] FIG. 4 shows a flow diagram of an example method for
receiving a selection of a physical object to be removed in an
image, according to some example embodiments.
[0009] FIG. 5 shows a flow diagram of an example method for
receiving a selection of a physical object to be removed in an
image using segmentation, according to some example
embodiments.
[0010] FIG. 6 shows an example flow diagram of a method for
generating a render of a virtual item in an arranged pose,
according to some example embodiments.
[0011] FIG. 7 shows a flow diagram of a method for orchestration of
virtual light sources based on a user's real-world environment,
according to some example embodiments.
[0012] FIG. 8 shows an example user interface for removing a
physical item, according to some example embodiments.
[0013] FIGS. 9A-9C show example user interfaces and mask data for
selecting a physical item, according to some example
embodiments.
[0014] FIG. 10 shows an example user interface depicting an image
of the physical item removed via image manipulation, according to
some example embodiments.
[0015] FIG. 11 shows an example user interface for determining
light sources, according to some example embodiments.
[0016] FIG. 12 shows an illustrative example of a physical room
used for light source positioning, according to some example
embodiments.
[0017] FIG. 13 shows an example user interface depicting a
modified. image, according to some example embodiments.
[0018] FIGS. 14.A-14C show image segmentation for physical object
selection, according to some example embodiments.
[0019] FIG. 15 is a block diagram illustrating components of a
machine, according to some example embodiments, able to read
instructions from a machine-readable medium (e.g., a
machine-readable storage medium) and perform any one or more of the
methodologies discussed herein.
DETAILED DESCRIPTION
[0020] The description that follows includes systems, methods,
techniques, instruction sequences, and computing machine program
products that embody illustrative embodiments of the disclosure. In
the following description, for the purposes of explanation,
numerous specific details are set forth in order to provide an
understanding of various embodiments of the inventive subject
matter. It will be evident, however, to those skilled in the art,
that embodiments of the inventive subject matter may be practiced
without these specific details. In general, well-known instruction
instances, protocols, structures, and techniques are not
necessarily shown in detail.
[0021] With reference to FIG. 1, an example embodiment of a
high-level client-server-based network architecture 100 is shown. A
networked system 102, in the example form of a network-based
rendering platform, can provide server-side rendering via a network
104 (e.g., the Internet or a wide area network (WAN)) to one or
more client devices 110. In some implementations, a user 106
interacts with the networked system 102 using the client device
110. The client device 110 may execute a physical item replacement
system 150 as a local application or a cloud-based application
(e.g., through an Internet browser).
[0022] In various implementations, the client device 110 comprises
a computing device that includes at least a display and
communication capabilities that provide access to the networked
system 102 via the network 104. The client device 110 comprises,
but is not limited to, a remote device, work station, computer,
general-purpose computer, Internet appliance, hand-held device,
wireless device, portable device, wearable computer, cellular or
mobile phone, personal digital assistant (PDA), smart phone,
tablet, ultrabook, netbook, laptop, desktop, multi-processor
system, microprocessor-based or programmable consumer electronic
system, game console, set-top box, network personal computer (PC),
mini-computer, and so forth. In an example embodiment, the client
device 110 comprises one or more of a touch screen, accelerometer,
gyroscope, biometric sensor, camera (e.g., an RGB based camera, a
depth sensing camera), microphone, Global Positioning System (GPS)
device, and the like.
[0023] The client, device 110 communicates with the network 104 via
a wired or wireless connection. For example, one or more portions
of the network 104 comprise an ad hoc network, an intranet, an
extranet, a virtual private network (VPN), a local area network
(LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless
WAN (WWAN), a metropolitan area network (MAN), a portion of the
Internet, a portion of the public switched telephone network
(PSTN), a cellular telephone network, a wireless network, a
WI-FI.RTM. network, a Worldwide Interoperability for Microwave
Access (WiMax) network, another type of network, or any suitable
combination thereof.
[0024] Users (e.g., the user 106) comprise a person, a machine, or
other means of interacting with the client device 110. In some
example embodiments, the user 106 is not part of the network
architecture 100, but interacts with the network architecture 100
via the client device 110 or another means. For instance, the user
106 provides input (e.g., touch-screen input or alphanumeric input)
to the client device 110 and the input is communicated to the
networked system 102 via the network 104. In this instance, the
networked system 102, in response to receiving the input from the
user 106, communicates information to the client device 110 via the
network 104 to be presented to the user 106. In this way, the user
106 can interact with the networked system 102 using the client
device 110.
[0025] An API server 120 and a web server 122 are coupled to, and
provide programmatic and web interfaces respectively to, one or
more application servers 140. The application server 140 can host a
physical item replacement system 150, which can comprise one or
more modules or applications, and which can be embodied as
hardware, software, firmware, or any combination thereof. The
application server 140 is, in turn, shown to be coupled to a
database server 124 that facilitates access to one or more
information storage repositories, such as a database 126. In an
example embodiment, the database 126 comprises one or more storage
devices that store information to be accessed by the physical item
replacement system 150. Additionally, in some embodiments, the
information may be cached locally on the client device 110.
Further, while the client-server-based network architecture 100
shown in FIG. 1 employs a client-server architecture, the present
inventive subject matter is, of course, not limited to such an
architecture, and can equally well find application in a
distributed, or peer-to-peer, architecture system, for example.
[0026] FIG. 2 shows example internal functional engines of a
physical item replacement system 150, according to some example
embodiments. As illustrated, the physical item replacement system
150 comprises a capture engine 205, a classification engine 207, a
removal engine 210, a mask engine 215, a pose engine 220, a light
engine 225, a model engine 227, and a display engine 230. The
capture engine 205 is configured to generate images (e.g., an
image, an image sequence, live video) using an image sensor of a
user device (e.g., a client device). The classification engine 207
manages classifying an object to be removed in an image. The
generated classification (e.g., object category) can be used to
recommend objects to replace the object (e.g., if the user selects
a chair, other chairs in a chair category can be displayed for
selection by the user). In some example embodiments, the generated
classification is used by the pose engine 220 to select a machine
learning scheme trained to detect poses for a certain class of
objects (e.g., a convolutional neural network trained to detect
poses of chairs, another convolutional scheme trained to detect the
poses of an articulating-arm floor lamp, etc.).
[0027] The removal engine 210 is configured to receive a selection
of a region in an image and remove an object depicted in the region
using areas surrounding the selected region. For example, the
removal engine 210 can generate an image mask for a given image
that indicates which region includes the object to be replaced
(e.g., the mask is used to denote or create an image hole in the
original image to be filled in via inpainting or other
interpolation approaches).
[0028] The mask engine 215 is configured to generate the mask data
based on an input selection received from the user. For example,
the user can perform a circle gesture on the item depicted on a
touch screen to indicate that the encircled image is to be removed,
or the user can tap on the item and a segmented portion of the
image that contains the depicted item is then stored as the mask
area. In some example embodiments, the mask engine 215 comprises an
image segmentation neural network that segments an image into
different areas. The segmented areas can then be selected via
tapping, as described above.
[0029] The pose engine 220 is configured to determine the pose of a
selected item to be removed. The determined pose is then used to
arrange the virtual item that is to replace the removed item in the
same pose. In some example embodiments, the pose engine 220 is
trained on images of different classes of objects (e.g., images of
chairs and lamps), and the pose engine 220 attempts to generate the
pose data using the model for a given object type (e.g., if a chair
object category is detected, the pose engine 220 applies a neural
network model that has been trained on images of chair
poses/orientations). As such, according to some example
embodiments, the pose engine 220 comprises a plurality of pose
detection neural networks, where each neural network is trained for
a different type of object.
[0030] The light engine 225 manages detecting light sources in an
image, which can be used by the model engine 227 to position
virtual light sources for virtual object rendering. The model
engine 227 is configured to manage a virtual 3D modeling
environment for rendering of a virtual item for overlay over the
image captured by the capture engine 205. The display engine 230 is
configured to generate user interfaces for interaction with a user
of a client device, and receive interactions (e.g., selection of a
region in an image) from the user through said user interfaces.
[0031] FIG. 3 shows a flow diagram of an example method 300 for
physical item replacement, according to some example embodiments.
It is to be appreciated that, although in the example here only a
single image is discussed, in some example embodiments, the image
is one frame of an image sequence or live video. In those example
embodiments, the method 300 is applied to each frame of the
sequence, and such frames are displayed on the user device's screen
in real time or near-real time. At operation 305, the capture
engine 205 generates an image. For example, the image can be of a
chair in a room. At operation 310, the removal engine 210 receives
a selection of a region in the image depicting an object to be
removed. For example, the user may tap on the chair in the image,
or may scribble on the chair in the image, as discussed in further
detail below. The received input can then be used to generate an
image mask that indicates a hole region to be filled in by
surrounding areas.
[0032] At operation 315, the removal engine 210 removes the object
from the image. In some example embodiments, at operation 315 the
removal engine 210 removes the object by merging areas surrounding
the image into the image area (e.g., inpainting, interpolation). At
operation 320, the model engine 227 generates a render of a virtual
object to replace the removed object in the image. For example,
after the object in the image has been removed via inpainting, the
model engine 227 generates a render of a 3D chair model for
integration into the image. At operation 325, the model engine 227
generates a modified image by overlaying and integrating (e.g.,
blending) the render into the image.
[0033] FIG. 4 shows a flow diagram of an example method 400 for
receiving a selection of a physical object to be removed in an
image, according to some example embodiments. As illustrated, the
operations of the method 400 may be implemented as a subroutine of
operation 310 of the method 300 of FIG. 3, in which a selection of
an image is received. At operation 405, the removal engine 210
receives user input on the image specifying a region of the image
depicting the object to be removed. For example, while the image is
displayed on the display device, the user can tap on a depicted
chair to be removed, drag a shape (e.g., a rectangle) around the
depicted chair, perform a circular gesture around the depicted
chair to roughly outline it, or scribble over the chair, according
to some example embodiments.
[0034] At operation 410, the mask engine 215 generates an image
mask from the specified region. For example, if the user drags a
rectangular UI shape element over the depicted chair, then at
operation 410 the mask engine 215 generates an image mask where the
pixels of the rectangular region are masked (e.g., set to "0")
while the surrounding areas are unaltered or set to another value
(e.g., set to "1"). After the user input is received and region
data stored (e.g., stored as an image mask), the stored region data
can be input into a machine learning scheme to remove the depicted
physical object from the image. In some example embodiments, at
operation 410 the mask data is applied to the image to create a
"hole" in the image corresponding to the masked areas. For example,
all pixels in the original image of the chair denoted by the
rectangular region can be deleted or otherwise removed to create a
hole in the original image where the chair was originally depicted.
According to sonic example embodiments, the original image with the
hole created by the image mask is the data used for inpainting and
interpolation.
[0035] FIG. 5 shows a flow diagram of an example method 500 for
receiving a selection of a physical object to be removed. In an
image using segmentation, according to some example embodiments.
Image segmentation is a computational task in which an image
segmentation neural network identifies different regions of an
image (e.g., a face region, an eye region, a background region, a
foreground region, etc.) and labels the pixels of each region
(e.g., generates a mask for each region) for later processing
(e.g., image manipulation of a given region). The operations of the
method. 500 may be implemented as a subroutine of operation 310 of
the method 300 in FIG. 3, in which a selection of the item is
received.
[0036] At operation 505, the removal engine 210 segments an image
into regions. For example, the removal engine 210 implements a
convolutional neural network trained to perform image segmentation
to label different areas of an image (e.g., a background area, a
chair area, a human face area, etc.) and create masks to denote the
different regions/segments. At operation 510, the mask engine 215
receives a selection within the image. For example, the user may
tap or mouse click on a chair to be removed in the image. At
operation 515, the removal engine 210 identifies the region
corresponding to the selection. For example, if, at operation 510,
the user selects any pixel depicting a chair region, then at
operation 515 the removal engine 210 identifies all pixels labeled
as being a chair region at operation 505, or selects an image mask
for the chair region. At operation 520, the mask engine 215 stores
the pixel data of the region for input into the neural network for
object removal. For example, at operation 520, the mask engine 215
stores an image mask for the region selected via a tap gesture.
100371 FIG. 6 shows an example flow diagram of a method 600 for
generating a render of a virtual item in an arranged pose,
according to some example embodiments. The operations of the method
600 may be implemented as a subroutine of operation 320 of the
method 300 in FIG. 3, in which a render of a virtual object is
generated. At operation 605, the classification engine 207
classifies the depicted object to determine a classification or
category for the depicted object. For example, at
[0037] Attorney Docket No. 4536.018US2 10 operation 605 the
classification engine 207 determines that the selected object is a
type of chair and therefore generates and stores a chair category
for the item. At operation 610, the pose engine 220 selects a pose
estimation scheme based on the classification generated at
operation 605. For example, at operation 610, the pose engine 220
selects a convolutional neural network trained to detect chair
poses based on chair training images.
[0038] At operation 615, the pose engine 220 determines the pose of
the depicted physical object. For example, at operation 615 the
pose engine 220 applies the selected machine learning scheme for
the given classification assigned to the depicted object to
determine that the chair backside is facing the wall, away from the
user at an angle.
[0039] At operation 620, the model engine 227 arranges the virtual
object to match the pose of the depicted physical object. For
example, the model engine 227 arranges a chair 3D virtual model so
that the backside of the chair is not facing the virtual camera
(where the virtual camera is set by the user's perspective, as
discussed in further detail with reference to FIG. 12 below.)
[0040] At operation 625, the model engine 227 arranges virtual
light sources in a modeling environment (e.g., a 3D model rendering
environment executing on the user device) to cast virtual light
rays on the virtual item to mimic the real-world environment
depicted in the image (e.g., the room being imaged and displayed in
real time on the display device). At operation 630, the model
engine 227 generates a render of the arranged and virtually
illuminated virtual item, which can then be blended into the image
and displayed on the mobile device screen.
[0041] FIG. 7 shows a flow diagram of a method 700 for
orchestration of virtual light sources based on a user's real-world
environment, according to some example embodiments. The operations
of the method 700 may be implemented as a subroutine of operation
625 of the method 600 in FIG. 6, in which lighting is
configured.
[0042] At operation 705, the light engine 225 separates the image
into regions, such as a top left region, a top right region, a
bottom left region, and a bottom right region. At operation 710,
the light engine 225 determines the brightest regions based on
luminance or pixel values in the regions. For example, the light
engine 225 determines that the top right region is the brightest
region. At operation 715, the light engine 225 stores virtual light
position data (e.g., top right region as the brightest region), and
the model engine 227 uses the position data to position a virtual
light in the upper right portion of the virtual room (e.g., above
and to the right of a virtual item in the modeling environment).
For example, the light engine 225 can further store subarea
position data indicating that, within the top right region, the top
left portion is brightest, thereby indicating to the model engine
227 to position a virtual light source to correspond to the subarea
position data.
[0043] FIG. 8 shows an example user interface 802 of client device
110 for removing a physical item, according to some example
embodiments. The user interface 802 includes an image 803 of a
chair 800 sitting on the ground 805 in front of a wall 810. The
chair 800 is an example of a depicted physical item that a user of
the client device 110 wishes to replace with a virtual chair to
view how the virtual chair would look in the room. According to
some example embodiments, to initiate the method 300 discussed
above, the user selects a button 807 in the user interface 802 to
initiate physical object replacement.
[0044] FIGS. 9A-9C show example user interfaces and mask data for
selecting a physical item, according to some example embodiments.
In FIG. 9A, the user of the client device 110 selects the chair
800, within the image 803, for removal by performing a drag or
swipe gesture over the chair 800 to create a polygon shape 900 that
approximately circumscribes the object be removed, i.e., the chair
800. Upon the polygon shape 900 being created, the mask engine 215
generates an image mask using the polygon shape 900 and creates a
hole in the image 803 for interpolation or inpainting as discussed
above.
[0045] FIG. 9B shows an example image mask 905, according to some
example embodiments. As illustrated, the image mask 905 may have
the same image size (width and height) as the image 803 and include
a mask region 907 corresponding to the shape created by the user
inputting the polygon shape. The mask region 907 can be implemented
as input data for an inputting scheme, or can be used to delete or
otherwise remove the chair region within the image 803, thereby
creating a modified version of the image 803 with the chair region
removed.
[0046] FIG. 9C shows an additional approach for selecting the
physical item, according to some example embodiments. In FIG. 9C,
the user of the client device 110 selects the chair 800 for removal
by performing a scribble gesture on the image over the depicted
chair 800. The mask engine 215 then stores an arbitrary shape 910
(e.g., line data, a user interface (UI) scribble) that describes
the approximate region of pixels that depict the object to be
removed. In some example embodiments, the mask engine 215 adds
padding on both sides of the arbitrary shape 910 to "thicken" the
line (e.g., the original line may be two pixels in thickness and
may be thickened via padding of five pixels on both sides of the
line, thereby creating a twelve-pixel thickened line). The padded
arbitrary shape 910 is then applied to the original image to create
a hole or masked area in the shape of the arbitrary shape 910 for
interpolation and inpainting.
[0047] As illustrated by the examples of FIG. 9A and FIG. 9C, the
shape or input used to indicate the chair to be removed can be
roughly input (e.g., sloppy) and not include portions of the chair
(e.g., the portion of the chair legs below the shape 900, or
regions of the chair not removed by the arbitrary shape 910). While
the shape input and the resulting mask may not completely remove
the object from the image, the removal engine 210 can implement an
interpolation or inpainting technique that is contextually aware of
remaining chair segments such that the chair is completely removed
or inpainted over via the removal engine 210, as discussed in
further detail below.
[0048] FIG. 10 shows an example user interface depicting an image
1000 of the physical item removed via image manipulation, according
to some example embodiments. As discussed, in some example
embodiments, the removal engine 210 implements image merging
techniques that patch over a specified area of the image. In some
example embodiments, the removal engine 210 paints over the hole
area of the image using pixel colorations from nearby areas. In
some example embodiments, the removal engine 210 implements a patch
based-matching scheme (e.g., PatchMatch algorithm) to find
correspondences between regions of the missing area (e.g., the
hole) and the surrounding areas and replace the missing area with
image data from nearby areas. In some example embodiments, the
removal engine 210 implements a partial convolutional inpainting
neural network (e.g., partial convolution inpainting), in which the
partial convolutions at each layer of the network are updated to
remove masking where a given partial convolution is able to operate
on unmasked data. In some example embodiments, the removal engine
210 implements a diffusian based inpainting scheme (e.g., Navier
Strokes) to fill missing areas in the images.
[0049] FIG. 11 shows an example user interface 1102 for determining
light sources, according to some example embodiments. In the
example illustrated, the image with the chair removed is separated
into regions 1105-1120. The light engine 225 then analyzes the
pixel values of each region to determine approximate locations of
light sources. For example, the light engine 225 averages the pixel
values of each of the regions 1105-1120 to determine that the
region 1105 is the brightest and the region 1110 is the second
brightest. Further, the light engine 225 identifies portions within
each of the regions 1105 and 1110 that are brightest to determine
the directions of likely light sources. For example, the light
engine 225 can partition the region 1105 into four additional areas
(as denoted by the additional dotted lines in the region 1105), and
further determine that the upper left portion of the region 1105 is
brightest to determine that a light source is likely above and to
the left of the client device 110. In the example illustrated, the
light engine 225 further determines that another brightest region
in the top right portion of the region 1110 is the second-brightest
sub-region.
[0050] FIG. 12 shows an illustrative example of a physical room
used for light source positioning, according to some example
embodiments. A camera 1220 corresponds to an image sensor or client
device (e.g., smart phone) that generates the image in FIG. 11. The
real physical light sources include sunlight emanating from a
window 1227, and light corning from ceiling lights 1225 and 1230,
which collectively shine down on an object (e.g., a cube 1215, the
chair 800), thereby making certain regions of an image generated by
the camera 1220 brighter. In response to determining that one or
more regions of the image are brighter (e.g., via region data of
FIG. 11), the model engine 227 then positions virtual light sources
1222 and. 1226 above and to the right of the cube 1215 in a virtual
modeling environment. The virtual modeling environment is a 3D
modeling environment aligned to the room depicted in FIG. 12. For
example, a virtual wall can be created to correspond to the wall
810, a virtual floor can be created in the modeling environment to
correspond to the ground 805, and a virtual camera can be
positioned with respect to the virtual walls based on the
real-world positioning of the camera 1220 that generated the image
(e.g., a backside image camera of the client device 110).
[0051] In some example embodiments, image processing or rendering
techniques are implemented to simulate the lighting of the
environment, in addition to placement of virtual light sources. For
example, the image of the physical environment can be analyzed to
determine a lighting scheme (e.g., overall brightness or luminance
value of the image, identification of lighter and darker areas,
etc.) and the lighting scheme can be simulated by darkening the
render of the virtual object (e.g., darkening the texture surface,
darkening the spectral quality, reflectance, and so on) in addition
to simulating the lighting sources via virtual light source
placement. In this way, for example, a virtual render of a chair in
a shadowy corner can be first darkened using a global exposure
setting for the rendered object, and then virtual rays from one or
more virtual light sources can reflect off the virtual chair to
further increase simulation accuracy.
[0052] FIG. 13 shows an example user interface 1302 depicting a
modified image 1307, according to some example embodiments. After
placement of the virtual light sources 1222 and 1226 (FIG. 12), the
model engine 227 then arranges a three-dimensional model of a
virtual chair 1300 in the modeling environment in the same
arrangement as the original chair (e.g., backside against the wall
810). In some example embodiments, the pose engine 220 is
implemented to determine the pose of the physical chair (e.g.,
sitting on the ground 805 with the back of the chair 800 facing the
wall 810), and the model engine 227 arranges the virtual chair 1300
in the same pose for rendering, as illustrated. Further, due to
determination of real-world light sources and placement of
corresponding virtual light sources, the virtual chair is
realistically illuminated and appears to be a real-world object in
the physical room.
[0053] FIGS. 14A-14C show image segmentation for physical object
selection, according to some example embodiments. In FIG. 14A, an
image 1400 is of a couch 1405 and a chair 1410 in a room. In
response to the user initiating the physical item replacement
system 150 to replace an item (e.g., via selection of the button
807 in FIG. 8), the removal engine 210 implements a segmentation
neural network to segment areas of the image 1400. The segmented
areas are masks that denote different regions of the image 1400.
For example, with reference to FIG. 14B, the segmentation neural
network segments or labels all pixels depicting the couch 1405 as
"1" and labels all pixels depicting the chair 1410 as "2". The user
can then select a physical object for removal by selecting anywhere
within one of the segmented regions. For example, with reference to
FIG. 14C, the user can tap on the couch 1405 as indicated by a
circle UI element 1420, and the entire couch 1405 is stored as mask
data for input into the removal engine 210, as discussed above,
according to some example embodiments.
[0054] Certain embodiments are described herein as including logic
or a number of components, modules, or mechanisms. Modules can
constitute either software modules (e.g., code embodied on a
machine-readable medium) or hardware modules. A "hardware module"
is a tangible unit capable of performing certain operations and can
be configured or arranged in a certain physical manner. In various
example embodiments, one or more computer systems (e.g., a
standalone computer system, a client computer system, or a server
computer system) or one or more hardware modules of a computer
system (e.g., a processor or a group of processors) can be
configured by software (e.g., an application or application
portion) as a hardware module that operates to perform certain
operations as described herein.
[0055] In some embodiments, a hardware module can be implemented
mechanically, electronically, or any suitable combination thereof.
For example, a hardware module can include dedicated circuitry or
logic that is permanently configured to perform certain operations.
For example, a hardware module can be a special-purpose processor,
such as a field-programmable gate array (FPGA) or an
application-specific integrated circuit (ASIC). A hardware module
may also include programmable logic or circuitry that is
temporarily configured by software to perform certain operations.
For example, a hardware module can include software executed by a
general-purpose processor or other programmable processor. Once
configured by such software, hardware modules become specific
machines (or specific components of a machine) uniquely tailored to
perform the Configured functions and are no longer general-purpose
processors. It will be appreciated that the decision to implement a
hardware module mechanically, in dedicated and permanently
configured circuitry, or in temporarily configured circuitry (e.g.,
configured by software) can be driven by cost and time
considerations.
[0056] Accordingly, the phrase "hardware module" should be
understood to encompass a tangible entity, be that an entity that
is physically constructed, permanently configured (e.g.,
hardwired), or temporarily configured (e.g., programmed) to operate
in a certain manner or to perform certain operations described
herein. As used herein, "hardware-implemented module" refers to a
hardware module. Considering embodiments in which hardware modules
are temporarily configured (e.g., programmed), each of the hardware
modules need not be configured or instantiated at any one instant
in time. For example, where a hardware module comprises a
general-purpose processor configured by software to become a
special-purpose processor, the general-purpose processor may be
configured as respectively different special-purpose processors
(e.g., comprising different hardware modules) at different times.
Software accordingly configures a particular processor or
processors, for example, to constitute a particular hardware module
at one instant of time and to constitute a different hardware
module at a. different instant of time.
[0057] Hardware modules can provide information to, and receive
information from, other hardware modules. Accordingly, the
described hardware modules can be regarded as being communicatively
coupled. Where multiple hardware modules exist contemporaneously,
communications can be achieved through signal transmission (e.g.,
over appropriate circuits and buses) between or among two or more
of the hardware modules. In embodiments in which multiple hardware
modules are configured or instantiated at different times,
communications between such hardware modules may be achieved, for
example, through the storage and retrieval of information in memory
structures to which the multiple hardware modules have access. For
example, one hardware module can perform an operation and store the
output of that operation in a memory device to which it is
communicatively coupled. A further hardware module can then, at a
later time, access the memory device to retrieve and process the
stored output. Hardware modules can also initiate communications
with input or output devices, and can operate on a resource (e.g.,
a collection of information).
[0058] The various operations of example methods described herein
can be performed, at least partially, by one or more processors
that are temporarily configured (e.g., by software) or permanently
configured to perform the relevant operations. Whether temporarily
or permanently configured, such processors constitute
processor-implemented modules that operate to perform one or more
operations or functions described herein. As used herein,
"processor-implemented module" refers to a hardware module
implemented using one or more processors. 100591 Similarly, the
methods described herein can be at least partially
processor-implemented, with a particular processor or processors
being an example of hardware. For example, at least some of the
operations of a method can be performed by one or more processors
or processor-implemented modules. Moreover, the one or more
processors may also operate to support performance of the relevant
operations in a "cloud computing" environment or as a "software as
a service" (SaaS). For example, at least some of the operations may
be performed by a group of computers (as examples of machines
including processors), with these operations being accessible via a
network 104 (e.g., the Internet) and via one or more appropriate
interfaces (e.g., an application programming interface (API)).
[0059] The performance of certain of the operations may be
distributed among the processors, not only residing within a single
machine, but deployed across a number of machines In some example
embodiments, the processors or processor-implemented modules can be
located in a single geographic location (e.g., within a home
environment, an office environment, or a server farm). In other
example embodiments, the processors or processor-implemented
modules are distributed across a number of geographic
locations.
[0060] FIG. 15 is a block diagram illustrating components of a
machine 1500, according to some example embodiments, able to read
instructions from a machine-readable medium (e.g., a
machine-readable storage medium) and perform any one or more of the
methodologies discussed herein. Specifically, FIG. 15 shows a
diagrammatic representation of the machine 1500 in the example form
of a computer system, within which instructions 1516 (e.g.,
software, a program, an application, an applet, an app, or other
executable code), for causing the machine 1500 to perform any one
or more of the methodologies discussed herein, can be executed. For
example, the instructions 1516 can cause the machine 1500 to
execute the flow diagrams of FIGS. 3-7. Additionally, or
alternatively, the instructions 1516 can implement the capture
engine 205, the classification engine 207, the removal engine 210,
the mask engine 215, the pose engine 220, the light engine 225, the
model engine 227, and the display engine 230 of FIG. 2, and so
forth. The instructions 1516 transform the general, non-programmed
machine 1500 into a particular machine programmed to carry out the
described and illustrated functions in the manner described. In
alternative embodiments, the machine 1500 operates as a standalone
device or can be coupled (e.g., networked) to other machines. In a
networked deployment, the machine 1500 may operate in the capacity
of a server machine or a client machine in a server-client network
environment, or as a peer machine in a peer-to-peer (or
distributed) network environment. The machine 1500 can comprise,
but not be limited to, a server computer, a client computer, a
personal computer (PC), a tablet computer, a laptop computer, a
netbook, a set-top box (STB), a personal digital assistant (PDA),
an entertainment media system, a cellular telephone, a smart phone,
a mobile device, a wearable device (e.g., a smart watch), a smart
home device (e.g., a smart appliance), other smart devices, a web
appliance, a network router, a network switch, a network bridge, or
any machine capable of executing the instructions 1516,
sequentially or otherwise, that specify actions to be taken by the
machine 1500. Further, while only a single machine 1500 is
illustrated, the term "machine" shall also be taken to include a
collection of machines 1500 that individually or jointly execute
the instructions 1516 to perform any one or more of the
methodologies discussed herein. 100621 The machine 1500 can include
processors 1510, memory/storage 1530, and I/O components 1550,
which can be configured to communicate with each other such as via
a bus 1502. In an example embodiment, the processors 1510 (e.g., a
central processing unit (CPU), a reduced instruction set computing
(RISC) processor, a complex instruction set computing (CISC)
processor, a graphics processing unit (GPU), a digital signal
processor (DSP), an application-specific integrated circuit (ASIC),
a radio-frequency integrated circuit (RFIC), another processor, or
any suitable combination thereof) can include, for example, a
processor 1512 and a processor 1514 that may execute the
instructions 1516. The term "processor" is intended to include
multi-core processors 1510 that may comprise two or more
independent processors 1512, 1514 (sometimes referred to as
"cores") that can execute the instructions 1516 contemporaneously.
Although FIG. 15 shows multiple processors 1510, the machine 1500
may include a single processor 1510 with a single core, a single
processor 1510 with multiple cores (e.g., a multi-core processor
1510), multiple processors 1510 with a single core, multiple
processors 1510 with multiple cores, or any combination
thereof.
[0061] The memory/storage 1530 can include a memory 1532, such as a
main memory, or other memory storage, and a storage unit 1536, both
accessible to the processors 1510 such as via the bus 1502. The
storage unit 1536 and memory 1532 store the instructions 1516
embodying any one or more of the methodologies or functions
described herein. The instructions 1516 can also reside, completely
or partially, within the memory 1532, within the storage unit 1536,
within at least one of the processors 1510 (e.g., within the
processor's cache memory), or any suitable combination thereof,
during execution thereof by the machine 1500. Accordingly, the
memory 1532, the storage unit 1536, and the memory of the
processors 1510 are examples of machine-readable media.
[0062] As used herein, the term "machine-readable medium" means a
device able to store the instructions 1516 and data temporarily or
permanently and may include, but not be limited to, random-access
memory (RAM), read-only memory (ROM), buffer memory, flash memory,
optical media, magnetic media, cache memory, other types of storage
(e.g., erasable programmable read-only memory (EPROM)), or any
suitable combination thereof. The term "machine-readable medium"
should be taken to include a single medium or multiple media (e.g.,
a centralized or distributed database, or associated caches and
servers) able to store the instructions 1516. The term
"machine-readable medium" shall also be taken to include any
medium, or combination of multiple media, that is capable of
storing instructions (e.g., the instructions 1516) for execution by
a machine (e.g., the machine 1500), such that, the instructions,
when executed by one or more processors of the machine (e.g., the
processors 1510), cause the machine to perform any one or more of
the methodologies described herein. Accordingly, a
"machine-readable medium" refers to a single storage apparatus or
device, as well as "cloud-based" storage systems or storage
networks that include multiple storage apparatus or devices. The
term "machine-readable medium" excludes signals per se.
[0063] The 110 components 1550 can include a wide variety of
components to receive input, provide output, produce output,
transmit information, exchange information, capture measurements,
and so on. The specific I/O components 1550 that are included in a
particular machine will depend on the type of machine. For example,
portable machines such as mobile phones will likely include a touch
input device or other such input mechanisms, while a headless
server machine will likely not include such a touch input device.
It will be appreciated that the I/O components 1550 can include
many other components that are not shown in FIG. 15. The I/O
components 1550 are grouped according to functionality merely for
simplifying the following discussion, and the grouping is in no way
limiting. In various example embodiments, the I/O components 1550
can include output components 1552 and input components 1554. The
output components 1552 can include visual components (e.g., a
display such as a plasma display panel (PDP), a light-emitting
diode (LED) display, a liquid crystal display (LCD), a projector,
or a cathode ray tube (CRT)), acoustic components (e.g., speakers),
haptic components (e.g., a vibratory motor, resistance mechanisms),
other signal generators, and so forth. The input components 1554
can include alphanumeric input components (e.g., a keyboard, a
touch screen configured to receive alphanumeric input, a
photo-optical keyboard, or other alphanumeric input components),
point-based input components (e.g., a mouse, a touchpad, a
trackball, a joystick, a motion sensor, or other pointing
instruments), tactile input components (e.g., a physical button, a
touch screen that provides location and force of touches or touch
gestures, or other tactile input components), audio input
components (e.g., a microphone), and the like. 100661 In further
example embodiments, the I/O components 1550 can include biometric
components 1556, motion components 1558, environmental components
1560, or position components 1562 among a wide array of other
components. For example, the biometric components 1556 can include
components to detect expressions (e.g., hand expressions, facial
expressions, vocal expressions, body gestures, or eye tracking),
measure biosignals (e.g., blood pressure, heart rate, body
temperature, perspiration, or brain waves), identify a person
(e.g., voice identification, retinal identification, facial
identification, fingerprint identification, or
electroencephalogram-based identification), and the like. The
motion components 1558 can include acceleration sensor components
(e.g., an accelerometer), gravitation sensor components, rotation
sensor components (e.g., a gyroscope), and so forth. The
environmental components 1560 can include, for example,
illumination sensor components (e.g., a photometer), temperature
sensor components (e.g., one or more thermometers that detect
ambient, temperature), humidity sensor components, pressure sensor
components (e.g., a barometer), acoustic sensor components (e.g.,
one or more microphones that detect background noise), proximity
sensor components (e.g., infrared sensors that detect nearby
objects), gas sensor components (e.g., machine olfaction detection
sensors, gas detection sensors to detect concentrations of
hazardous gases for safety or to measure pollutants in the
atmosphere), or other components that may provide indications,
measurements, or signals corresponding to a surrounding physical
environment. The position components 1562 can include location
sensor components (e.g., a Global Positioning System (GPS) receiver
component), altitude sensor components (e.g., altimeters or
barometers that detect air pressure from which altitude may be
derived), orientation sensor components (e.g., magnetometers), and
the like.
[0064] Communication can be implemented using a wide variety of
technologies. The I/O components 1550 may include communication
components 1564 operable to couple the machine 1500 to a network
1580 or devices 1570 via a coupling 1582 and a coupling 1572,
respectively. For example, the communication components 1564
include a network interface component or other suitable device to
interface with the network 1580. In further examples, the
communication components 1564 include wired communication
components, wireless communication components, cellular
communication components, near field communication (NFC)
components, BLUETOOTH.RTM. components e.g., BLUETOOTH.RTM. Low
Energy), WI-FI.RTM. components, and other communication components
to provide communication via other modalities. The devices 1570 may
be another machine or any of a wide variety of peripheral devices
(e.g., a peripheral device coupled via a Universal Serial Bus
(USB)).
[0065] Moreover, the communication components 1564 can detect
identifiers or include components operable to detect identifiers.
For example, the communication components 1564 can include radio
frequency identification (RFID) tag reader components, NFC smart
tag detection components, optical reader components (e.g., an
optical sensor to detect one-dimensional bar codes such as a
Universal Product Code (UPC) bar code, multi-dimensional bar codes
such as a Quick Response (QR) code, Aztec Code, Data Matrix,
Dataglyph, MaxiCode, PDF117, Ultra Code, Uniform Commercial Code
Reduced Space Symbology (UCC RSS)-2D bar codes, and other optical
codes), acoustic detection components (e.g., microphones to
identify tagged audio signals), or any suitable combination
thereof. In addition, a variety of information can be derived via
the communication components 1564, such as location via Internet
Protocol (IP) geo-location, location via WITIO signal
triangulation, location via detecting a BLUETOOTH.RTM. or NFC
beacon signal that may indicate a particular location, and so
forth.
[0066] In various example embodiments, one or more portions of the
network 1580 can be an ad hoc network, an intranet, an extranet, a
virtual private network (VPN), a local area network (LAN), a
wireless LAN (WLAN), a wide area network (WAN), a wireless WAN
(WWAN), a metropolitan area network (MAN), the Internet, a portion
of the Internet, a portion of the public switched telephone network
(PSTN), a plain old telephone service (POTS) network, a cellular
telephone network, a wireless network, a WI-FIS network, another
type of network, or a combination of two or more such networks. For
example, the network 1580 or a portion of the network 1580 may
include a wireless or cellular network, and the coupling 1582 may
be a Code Division Multiple Access (CDMA) connection, a Global
System for Mobile communications (GSM) connection, or another type
of cellular or wireless coupling. In this example, the coupling
1582 can implement any of a variety of types of data transfer
technology, such as Single Carrier Radio Transmission Technology
(1xRTT), Evolution-Data Optimized (EVDO) technology, General Packet
Radio Service (GPRS) technology, Enhanced. Data rates for GSM
Evolution (EDGE) technology, third Generation Partnership Project
(3GPP) including 3G, fourth generation wireless (4G) networks,
Universal Mobile Telecommunications System (UMTS), High-Speed
Packet Access (HSPA), Worldwide Interoperability for Microwave
Access (WiMAX), Long-Term Evolution (LTE) standard, or others
defined by various standard-setting organizations, other long-range
protocols, or other data-transfer technology.
[0067] The instructions 1516 can be transmitted or received over
the network 1580 using a transmission medium via a network
interface device (e.g., a network interface component included in
the communication components 1564) and utilizing any one of a
number of well-known transfer protocols (e.g., Hypertext Transfer
Protocol (HTTP)). Similarly, the instructions 1516 can be
transmitted or received using a transmission medium via the
coupling 1572 (e.g., a peer-to-peer coupling) to the devices 1570.
The term "transmission medium" shall be taken to include any
intangible medium that is capable of storing, encoding, or carrying
the instructions 1516 for execution by the machine 1500, and
includes digital or analog communications signals or other
intangible media to facilitate communication of such software.
[0068] Throughout this specification, plural instances may
implement components, operations, or structures described as a
single instance. Although individual operations of one or more
methods are illustrated and described as separate operations, one
or more of the individual operations may be performed concurrently,
and nothing requires that the operations be performed in the order
illustrated. Structures and functionality presented as separate
components in example configurations may be implemented as a
combined structure or component. Similarly, structures and
functionality presented as a single component may be implemented as
separate components. These and other variations, modifications,
additions, and improvements fall within the scope of the subject
matter herein.
[0069] Although an overview of the inventive subject matter has
been described with reference to specific example embodiments,
various modifications and changes may be made to these embodiments
without departing from the broader scope of embodiments of the
present disclosure. Such embodiments of the inventive subject
matter may be referred to herein, individually or collectively, by
the term "invention" merely for convenience and without intending
to voluntarily limit the scope of this application to any single
disclosure or inventive concept if more than one is, in fact,
disclosed.
[0070] The embodiments illustrated herein are described in
sufficient detail to enable those skilled in the art to practice
the teachings disclosed. Other embodiments may be used and derived
therefrom, such that structural and logical substitutions and
changes may be made without departing from the scope of this
disclosure. The Detailed Description, therefore, is not to be taken
in a limiting sense, and the scope of various embodiments is
defined only by the appended. claims, along with the full range of
equivalents to which such claims are entitled.
[0071] As used herein, the term "or" may be construed in either an
inclusive or exclusive sense. Moreover, plural instances may be
provided for resources, operations, or structures described herein
as a single instance. Additionally, boundaries between various
resources, operations, modules, engines, and data stores are
somewhat arbitrary, and particular operations are illustrated in a
context of specific illustrative configurations. Other allocations
of functionality are envisioned and may fall within a scope of
various embodiments of the present disclosure. In general,
structures and functionality presented as separate resources in the
example configurations may be implemented as a combined structure
or resource. Similarly, structures and functionality presented as a
single resource may be implemented as separate resources. These and
other variations, modifications, additions, and improvements fall
within a scope of embodiments of the present disclosure as
represented by the appended claims. The specification and drawings
are, accordingly, to be regarded in an illustrative rather than a
restrictive sense.
* * * * *