DETAILED ACTION
Claims 15-34 are pending in the present application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d). The certified copy of China patent application number CN202210626337.8 filed on 06/02/2022 has been received and made of record.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/02/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 15, 17-19, 24-25, 27-29, and 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over China PGPub CN112002014A to Zhu et al. in view U.S. PGPubs 2021/0390789 to Liu et al..
Regarding claim 15, Zhu et al. teach a method for image processing (abstract), comprising:
performing three-dimensional reconstruction on an original face image to obtain an initial 3D model (par 0007, “obtaining a two-dimensional face image to be reconstructed as an input image; …performing key point detection on the input image and fitting the parameters of the 3DMM model to obtain a three-dimensional space transformation function and an initial three-dimensional face shape”, par 0048, “Based on the collected RGB-D image, the present invention performs key point detection on the image and fits a 3DMM model to obtain the initial three-dimensional face shape” …..generate a initial 3D model based on 2D face image);
transforming the initial 3D model according to predetermined transformation information to obtain a transformed 3D model (par 0007-0011, “perform spatial transformation on the initial three-dimensional face shape according to the spatial transformation function, obtain the position of each point on the input image on the fitted 3DMM face model, and convert the input image face according to the position ….Each shape update amount is added to each point corresponding to the initial 3D face shape to obtain a 3D reconstruction result corresponding to the input image”, par 0039-0040, “The fitting module is configured to perform key point detection on the input image and fit parameters of the 3DMM model to obtain a three-dimensional space transformation function and an initial three-dimensional face shape; The mapping module is configured to perform spatial transformation on the initial three-dimensional face shape according to the spatial transformation function to obtain the position of each point on the fitted 3DMM face model on the input image“).
But Zhu et al. do not explicitly teach performing expansion processing on the transformed 3D model to obtain an expanded 3D model; determining a displacement image based on the expanded 3D model and the initial 3D model; and performing transformation processing on the original face image according to the displacement image to obtain a target face image.
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In related endeavor, Liu et al. teach performing expansion processing on the transformed 3D model to obtain an expanded 3D model (par 0128, “the process 1000 can include generating an extended 3D model (e.g., extended 3D face mesh 214) of the face. In some cases, the extended 3D model can be generated by extending the 3D model of the face to include one or more regions (e.g., background region 304, forehead region 306, hair region 308, etc.) beyond a boundary of the 3D model of the face (e.g., beyond or outside of the face region 302 (or a contour of the face region 302) included in the 3D face mesh 210, beyond or outside of a background region (or a contour of the background region) included in the 3D face mesh 210)”);
determining a displacement image based on the expanded 3D model and the initial 3D model (par 0081, “The image processing system 100 can use the depth information 212 to generate an extended 3D face mesh 214. The extended 3D face mesh 214 can augment face data in the 3D face mesh 210 to include data (e.g., depth information, texture information, shape/geometry information, etc.) associated with other regions of and/or around the face such as, for example, a forehead region, a hair region, a background region, etc. In some examples, the extended 3D face mesh 214 can include the 3D face mesh 210 with additional regions (e.g., extended to include additional regions) associated with the depth information 212”, par 0129-0131, “determining the depth information can include estimating depths of a first group of anchor points (e.g., first group of anchors 320) on a face region and a second group of anchor points (e.g., second group of anchors 322) on and/or included in the forehead region. In some cases, determining the depth information can also include estimating depths of a third group of anchor points (e.g., third group of anchors 324), a fourth group of anchor points (e.g., fourth group of anchors 326), and/or a fifth group of anchor points (e.g., fifth group of anchors 328) on one or more regions surrounding at least a portion of the face. In some cases, a depth of an anchor point is estimated based on a first location of the anchor point within the 3D model and a distance of the anchor point to a second location on the face, such as a point on the face, a center of the face, and/or any other location on the face”); and
performing transformation processing on the original face image according to the displacement image to obtain a target face image (par 0128-0131, “the process 1000 can include generating, based on the extended 3D model of the face, a second image (e.g., output image 216) depicting the face in a rotated position relative to a position of the face in the first image. In some examples, the face can be rotated in a different direction and/or angle relative to the position of the face in the first image. In some examples, the rotated position can include a different head pose. In some cases, generating the second image can include mapping the first image to the extended 3D model, and rotating the face in the mapping of the first image to the extended 3D model. The face can be rotated in the rotated position. ….the extended 3D model can be generated based on depth information (e.g., depth information 212) associated with the forehead region and/or the region surrounding at least a portion of the face. In some examples, the depth information can include a first depth of the forehead region and/or a second depth of the region surrounding at least a portion of the face. In some aspects, the process 1000 can include determining the depth information”).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Zhu et al. to include performing expansion processing on the transformed 3D model to obtain an expanded 3D model; determining a displacement image based on the expanded 3D model and the initial 3D model; and performing transformation processing on the original face image according to the displacement image to obtain a target face image as taught by Liu et al. to generate an extended 3D model of the face by extending the 3D model of the face to include one or more regions beyond a boundary of the 3D model of the face for face analytics to use in a wide array of applications such as, for example and without limitation, computer graphics, extended reality (XR) (e.g., including augmented reality (AR), virtual reality (VR), mixed reality (MR), etc.), facial recognition, emotion/expression detection, eye gaze estimation, age estimation, gender estimation, face rendering, face counting, among others.
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Regarding claim 17, Zhu et al. as modified by Liu et al. teach all the limitation of claim 15, and Liu et al. further teach wherein performing expansion processing on the transformed 3D model to obtain the expanded 3D model comprises: selecting a plurality of target vertices from the transformed 3D model; determining a plurality of expanded vertices respectively corresponding to the plurality of target vertices to obtain a plurality of expanded vertices; constructing a triangular mesh based on the plurality of target vertices and the plurality of expanded vertices to obtain an expanded mesh; and forming the expanded 3D model with the expanded mesh and the transformed 3D model (par 0128, “the process 1000 can include generating an extended 3D model (e.g., extended 3D face mesh 214) of the face. In some cases, the extended 3D model can be generated by extending the 3D model of the face to include one or more regions (e.g., background region 304, forehead region 306, hair region 308, etc.) beyond a boundary of the 3D model of the face (e.g., beyond or outside of the face region 302 (or a contour of the face region 302) included in the 3D face mesh 210, beyond or outside of a background region (or a contour of the background region) included in the 3D face mesh 210). In some examples, the one or more regions can include a forehead region (e.g., forehead region 306) and/or a region surrounding at least a portion of the face. In some cases, the region surrounding at least a portion of the face can include a hair and/or head region (e.g., hair region 308), a background region (e.g., background region 304), a neck region, and/or an ear region”, Figs 3A-3B, par 0088, par 0111-0112, “the extended 3D face mesh 214 can be generated using anchor points in a face region 302 (e.g., a face) of the 3D face mesh 210 to estimate anchor points in a background region 304, a forehead region 306 and a hair region 308 (and/or head region), and performing Delaunay triangulation to triangulate the anchor points in the face region 302, the background region 304, the forehead region 306 and the hair region 308. In the illustrative examples in FIGS. 3A and 3B, the extended 3D face mesh 214 includes Delaunay triangles 310 in the face region 302, the background region 304, the forehead region 306 and the hair region 308. The Delaunay triangles 310 can be used to extend the background region 304, the forehead region 306 and the hair region 308, and generate the extended 3D face mesh 214. The extended 3D face mesh 214 can include the face region 302 and the background region 304, forehead region 306 and hair region 308 modeled using the Delaunay triangles 310”).
Regarding claim 18, Zhu et al. as modified by Liu et al. teach all the limitation of claim 17, and Liu et al. further teach wherein determining the plurality of expanded vertices respectively corresponding to the plurality of target vertices comprises: obtaining a bounding rectangular box of the transformed 3D model; and determining an expanded vertex on an extension line of a line connecting a center point of the bounding rectangular box and the target vertex based on size information of the bounding rectangular box; wherein the size information of the bounding rectangular box comprises at least one of a width and a height (Fig 2, par 0070-0071, “The image processing system 100 can then generate a cropped image 204 containing or including image data and/or contents within the bounding box 220”, par 0125, “The face region can include the face of the user. In some examples, the face region can include a region within a bounding box (e.g., bounding box 220) around the face of the user. In some cases, the region within the bounding box can include the face of the user and one or more surrounding regions such as, for example, a hair region, a neck region, an ear region, a shoulder region, a background region, etc.”, par 0084-0085, “he depth information 212 and the extended 3D face mesh 214 can allow certain information (e.g., information identifying the face, a facial expression, one or more facial attributes, etc.) to be preserved in the output image 216….. to determine the depth information 212 and generate the extended 3D face mesh 214, the image processing system 100 can create n number of groups of anchors (e.g., nodes, points, etc.) around the face region in the 3D face mesh 210. The image processing system 100 can estimate the depth of the groups of anchors based on their locations and their relative distance to a center of the face“, Fig 3, par 0108-0111, “The forehead region 306 can be approximated based on the 4D head model 500. For example, a number of anchor points can be uniformly chosen for each anchor group in the forehead region 306 (e.g., the first forehead group of anchors 330, second forehead group of anchors 332, and third forehead group of anchors 334) based on the distance of the anchor points and the face center. In one illustrative example, 40 anchor points can be uniformly chosen for each anchor group in the forehead region 306. In other examples, more or less than 40 anchor points can be chosen for each anchor group within the forehead region 306. In some cases, anchor points that are not on the head and/or hair region, such as anchor points on the left, right, and bottom of the face region (e.g., on and/or around the ear and neck regions), may not be on the 3D face model (e.g., the 3D face mesh 210). The forehead region 306 can then be approximated by triangulating the anchor points from each anchor group in the forehead region 306 based on their 3D coordinates, as further described below …. In some examples, to smoothen the extended 3D face mesh 214, the depth values of left, right, and bottom anchor points on the second group of anchors 322, the third group of anchors 324, and fourth group of anchors 326 can be determined by multiplying the minimum face contour points depth values times x, y, and z multiplier values, respectively, where x is larger than y and y is larger than z. For example, the minimum face contour points depth values can be multiplied by 0.9 (e.g., x), 0.8 (e.g., y), and 0.7 (e.g., z), respectively”, par 0128, “the process 1000 can include generating an extended 3D model (e.g., extended 3D face mesh 214) of the face. In some cases, the extended 3D model can be generated by extending the 3D model of the face to include one or more regions (e.g., background region 304, forehead region 306, hair region 308, etc.) beyond a boundary of the 3D model of the face (e.g., beyond or outside of the face region 302 (or a contour of the face region 302) included in the 3D face mesh 210, beyond or outside of a background region (or a contour of the background region) included in the 3D face mesh 210)”).
Regarding claim 19, Zhu et al. as modified by Liu et al. teach all the limitation of claim 15, and Liu et al. further teach wherein determining the displacement image based on the expanded 3D model and the initial 3D model comprises: determining displacement information of a plurality of vertices based on the expanded 3D model and the initial 3D model; and generating the displacement image based on the displacement information (Fig 3B, par 0087-0088, par 0128-0133, “the process 1000 can include generating an extended 3D model (e.g., extended 3D face mesh 214) of the face. In some cases, the extended 3D model can be generated by extending the 3D model of the face to include one or more regions (e.g., background region 304, forehead region 306, hair region 308, etc.) beyond a boundary of the 3D model of the face (e.g., beyond or outside of the face region 302 (or a contour of the face region 302) included in the 3D face mesh 210, beyond or outside of a background region (or a contour of the background region) included in the 3D face mesh 210) …. At block 1010, the process 1000 can include generating, based on the extended 3D model of the face, a second image (e.g., output image 216) depicting the face in a rotated position relative to a position of the face in the first image. In some examples, the face can be rotated in a different direction and/or angle relative to the position of the face in the first image …. the extended 3D model can be generated based on depth information (e.g., depth information 212) associated with the forehead region and/or the region surrounding at least a portion of the face ….. determining the depth information can include estimating depths of a first group of anchor points (e.g., first group of anchors 320) on a face region and a second group of anchor points (e.g., second group of anchors 322) on and/or included in the forehead region. In some cases, determining the depth information can also include estimating depths of a third group of anchor points (e.g., third group of anchors 324), a fourth group of anchor points (e.g., fourth group of anchors 326), and/or a fifth group of anchor points (e.g., fifth group of anchors 328) on one or more regions surrounding at least a portion of the face. In some cases, a depth of an anchor point is estimated based on a first location of the anchor point within the 3D model and a distance of the anchor point to a second location on the face, such as a point on the face, a center of the face, and/or any other location on the face”).
Regarding claim 24, Zhu et al. as modified by Liu et al. teach all the limitation of claim 15, and Liu et al. further teach wherein performing the transformation processing on the original face image based on the displacement image to obtain the target face image comprises: obtaining initial coordinates of a pixel in the original face image and displacement information of the pixel in the displacement image; determining transformation coordinates based on the initial coordinates and the displacement information; and rendering a pixel value of the pixel to a position corresponding to the transformation coordinates to obtain the target face image (par 0081-0082, “The image processing system 100 can then generate an output image 216 based on the extended 3D face mesh 214 and the input image 202. In some examples, to generate the output image 216, the image processing system 100 can map the input image 202 onto the extended 3D face mesh 214. In some cases, the image processing system 100 can rotate a face in the input image 202 (e.g., the face modeled in the extended 3D face mesh 214) based on the mapping. For instance, the output image 216 can include a person from the input image 202 with a different (e.g., rotated) head pose “, par 0109-0111, “to smoothen the extended 3D face mesh 214, the depth values of left, right, and bottom anchor points on the second group of anchors 322, the third group of anchors 324, and fourth group of anchors 326 can be determined by multiplying the minimum face contour points depth values times x, y, and z multiplier values, respectively, where x is larger than y and y is larger than z. For example, the minimum face contour points depth values can be multiplied by 0.9 (e.g., x), 0.8 (e.g., y), and 0.7 (e.g., z), respectively. In some examples, a number of anchor points surrounding a left, right and bottom of the face can be selected (and/or included in) from the second group of anchors 322, the third group of anchors 324, and fourth group of anchors 326 (e.g., left, right, and bottom anchor points from the second group of anchors 322, the third group of anchors 324, and fourth group of anchors 326). The depth values of the number of anchor points from the second group of anchors 322, the third group of anchors 324, and fourth group of anchors 326 (e.g., left, right, and bottom anchor points) can by determined by multiplying the minimum face contour points depth values as described above. In some cases, the (X, Y) coordinates of the second group of anchors 322 can be computed based on the distance between the first group of anchors 320 (e.g., anchor points on the boundary of the face region 302) and the estimated 2D face center. The face center can be estimated by the average points of the 3D face mesh 210. The (X, Y) coordinates on the second group of anchors 322 can be estimated based on the distance of the face center and the first group of anchors 320. In some examples, the second group of anchors 322, the third group of anchors 324, and fourth group of anchors 326 can be respectively estimated by multiplying the distance between the first group of anchors 320 and the face center by respectively increasing values …. Once the 3D coordinates of the anchor points described above are determined, the 3D face mesh 210 can be extended based on the 3D coordinates of the anchor points. In some examples, a Delaunay triangulation can be performed to triangulate the anchor points and generate the extended 3D face mesh 214. FIG. 6 is a diagram illustrating an example Delaunay triangulation 600 used to triangulate anchor points and generate the extended 3D face mesh 214”).
Regarding claim 25, Zhu et al. teach an electronic device, comprising: at least one processor; a storage device configured for storing at least one program, the at least one program, when executed by the at least one processor, causing the at least one processor to implement acts (par 0044-0045). The remaining limitations of the claim are similar in scope to claim 15 and rejected under the same rationale.
Regarding claims 27-29, Zhu et al. as modified by Liu et al. teach all the limitation of claim 25, the claims 27-29 are similar in scope to claims 17-19 and are rejected under the same rational.
Regarding claim 34, Zhu et al. teach a non-transitory storage medium containing computer executable instructions which, when executed by a computer processor, are configured for performing acts (par 00130). The remaining limitations of the claim are similar in scope to claim 15 and rejected under the same rationale.
Claim(s) 16 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over China PGPub CN112002014A to Zhu et al. in view of U.S. PGPubs 2021/0390789 to Liu et al., further in view of China PGPub CN112734890A to Sun et al..
Regarding claim 16, Zhu et al. as modified by Liu et al. teach all the limitation of claim 15, but keep silent for teaching wherein transforming the initial 3D model according to the predetermined transformation information to obtain the transformed 3D model comprises: generating a transformation matrix according to the predetermined transformation information; and transforming the initial 3D model based on the transformation matrix to obtain the transformed 3D model.
In related endeavor, Sun et al. teach wherein transforming the initial 3D model according to the predetermined transformation information to obtain the transformed 3D model comprises: generating a transformation matrix according to the predetermined transformation information; and transforming the initial 3D model based on the transformation matrix to obtain the transformed 3D model (par 0007, “3D face alignment: reconstructing the target face and the driving face into three-dimensional point clouds respectively, and calculating pose parameters of the target face and the driving face; calculating rotation matrixes of a target face and a driving face through affine transformation, and rotationally aligning the spatial positions of the target face and the driving face”, par 0014, “As a preferred scheme of the face replacement method based on three-dimensional reconstruction, in step two, the pose parameters include the displacement and rotation angle of the target face and the driving face relative to the standard frontal face coordinate system, and the pose parameters of the target face form a transformation matrix PtargetThe pose parameters of the driving face form a transformation matrix Psource(ii) a Record the target face as XtargetRecording the driving face as XsourceThe mode of the alignment operation is as follows: ptarget*Xtarget＝Psource*Xsource。”).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Zhu et al. as modified by Liu et al. to include wherein transforming the initial 3D model according to the predetermined transformation information to obtain the transformed 3D model comprises: generating a transformation matrix according to the predetermined transformation information; and transforming the initial 3D model based on the transformation matrix to obtain the transformed 3D model as taught by Liu et al. to realize face changing with any rotation angle and complex expression through one or two face photos of the target person, and quickly realize the replacement requirement of any face without needing a large amount of image input of the target face and extra model training cost for the target face.
Regarding claim 26, Zhu et al. as modified by Liu et al. teach all the limitation of claim 25, the claim 26 is similar in scope to claim 16 and is rejected under the same rational.
Claim(s) 20-23 and 30-33 is/are rejected under 35 U.S.C. 103 as being unpatentable over China PGPub CN112002014A to Zhu et al. in view of U.S. PGPubs 2021/0390789 to Liu et al., further in view of U.S. PGPubs 2022/0284679 to Lin et al.
Regarding claim 20, Zhu et al. as modified by Liu et al. teach all the limitation of claim 15, but keep silent for teaching wherein performing transformation processing on the initial face image based on the displacement image to obtain the target face image comprises: performing blurring processing on the displacement image; and performing the transformation processing on the original face image based on the blurred displacement image to obtain the target face image.
In related endeavor, Lin et al. teach wherein performing transformation processing on the initial face image based on the displacement image to obtain the target face image comprises: performing blurring processing on the displacement image; and performing the transformation processing on the original face image based on the blurred displacement image to obtain the target face image (par 0011-0014, “determining a non-core region in the initial 3D facial mesh, the non-core region being a non-facial region in the initial 3D facial mesh; smoothing the non-core region in the initial 3D facial mesh to obtain a smoothed non-core region; and replacing the non-core region in the initial 3D facial mesh with the smoothed non-core region to obtain a 3D facial mesh of the target object.” par 0050, “After the initial 3D facial mesh 15 is obtained, a non-core region in the initial 3D facial mesh 15 needs to be smoothed to obtain a smoothed non-core region. Finally, the non-core region in the initial 3D facial mesh 15 is replaced with the smoothed non-core region to obtain a 3D facial mesh 12 of the target object”, par 0108-0112, “After the convex hull is enlarged, the target region is obtained. The non-facial key pixels are determined based on the target region, so that the subsequent smoothing processing is more accurate, and a smoothed non-core region is fused with the initial 3D facial mesh more naturally.”).
It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Zhu et al. as modified by Liu et al. to include wherein performing transformation processing on the initial face image based on the displacement image to obtain the target face image comprises: performing blurring processing on the displacement image; and performing the transformation processing on the original face image based on the blurred displacement image to obtain the target face image as taught by Lin et al. to performing Poisson reconstruction on the 3D point cloud data to obtain a 3D facial mesh of the target object through constructing, a non-core region in the initial 3D facial mesh is smoothed, so that the smoothed non-core region is used to replace the original non-core region to obtain a 3D facial mesh that achieves a better effect.
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Regarding claim 21, Zhu et al. as modified by Liu et al. and Lin et al. teach all the limitation of claim 20, and Lin et al. further teach wherein performing the blurring processing on the displacement image comprises: determining a blur radius; and performing the blurring processing on the displacement image based on the blur radius (Fig 10, par 0109-0112, “the dilation threshold is specified based on a resolution. FIG. 10 is a schematic diagram of a target region according to an embodiment of this application. A convex hull 101 is enlarged based on the dilation threshold to obtain a target region 102. Step 706c. Determine pixels in a region other than the target region in the facial texture image as the non-facial key pixels …. After the convex hull is enlarged, the target region is obtained. The non-facial key pixels are determined based on the target region, so that the subsequent smoothing processing is more accurate, and a smoothed non-core region is fused with the initial 3D facial mesh more naturally” ….determine smooth area around face).
Regarding claim 22, Zhu et al. as modified by Liu et al. and Lin et al. teach all the limitation of claim 21, and Lin et al. further teach wherein determining the blur radius comprises: dividing the displacement image into a facial area and a background area; determining a blur radius of the facial area as a first blur radius; and determining a blur radius of the background area as a second blur radius; wherein the second blur radius is greater than the first blur radius (Fig 10, par 0109-0114, “After the convex hull is enlarged, the target region is obtained. The non-facial key pixels are determined based on the target region, so that the subsequent smoothing processing is more accurate, and a smoothed non-core region is fused with the initial 3D facial mesh more naturally” ….after determine no-core region 102, further determine non-facial key pixels 103 (as background) around face).
Regarding claim 23, Zhu et al. as modified by Liu et al. and Lin et al. teach all the limitation of claim 22, and Lin et al. further teach wherein the second blur radius varies with a distance between a pixel in the background area and a center point of a face (Fig 10, par 0109-0114, “After the convex hull is enlarged, the target region is obtained. The non-facial key pixels are determined based on the target region, so that the subsequent smoothing processing is more accurate, and a smoothed non-core region is fused with the initial 3D facial mesh more naturally” …. determine non-facial key pixels 103 (as background) around face).
Regarding claims 30-33, Zhu et al. as modified by Liu et al. teach all the limitation of claim 25, the claims 30-33 are similar in scope to claims 20-23 and are rejected under the same rational.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jin Ge whose telephone number is (571)272-5556. The examiner can normally be reached 8:00 to 5:00.
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JIN . GE
Examiner
Art Unit 2619
/JIN GE/Primary Examiner, Art Unit 2619
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