DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/26/2026, 04/09/2026, and 04/28/2026 has/have been considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 9, 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sevastopolskiy et al (US 20220157014 A1), hereinafter Sevastopolskiy.
-Regarding claim 1, Sevastopolskiy discloses an image rendering method performed by an electronic device, the method comprising (Abstract; FIGS. 1-5): extracting a plurality of key frames from a video stream as input images of a target object (FIG. 2, S200 – S205; FIG. 4, S100; [0006]; [0036]); determining masks, reflection images, and camera parameters of the input images (FIG. 1, Mask, Albedo; FIGS. 2; FIG. 3, $205.4; ; [0025]; “The camera viewpoint … camera intrinsic parameters …”; [0028], “… segmentation mask, the lighting-related auxiliary parameters and the parameters …”; [0032], “… camera viewpoints … auxiliary parameters …”; [0033], “ … segmented foreground …”; [0035], “Auxiliary parameters may include, … flashlight color temperature, and albedo half-texture”); constructing a model corresponding to the target object based on the masks and the camera parameters (FIGS. 1, 3; [0006]; [0008]; [0011]; [0033]); mapping the model based on the reflection images, to obtain a map of the model (FIGS. 1-4; [0026], [0027], “The predicted albedo, normals, environmental shadow maps, and segmentation mask are fused into the relighted 3D portrait 25 according to lighting condition”; [0031]; [0036], “A predicted image (e.g., predicted albedo, normals, environmental shadow maps, and segmentation mask) for the camera viewpoint is obtained … Auxiliary face meshes with corresponding texture mapping are predicted at S110 … for each of the images (or frames) in the captured sequence … The corresponding texture mapping may include a specification of two-dimensional coordinates in the fixed, predefined texture space for every vertex of the mesh …”); and performing illumination restoration on the model based on the map, to obtain an output image of the target object (Abstract; FIGS. 1-4; [0011]; [0020]; [0027]; [0031]).
-Regarding claim 9, Sevastopolskiy discloses an electronic device, comprising: a memory configured to store a plurality of computer-executable instructions; and a processor configured to implement, when executing the plurality of computer- executable instructions stored in the memory, an image rendering method including (Abstract; FIGS. 1-5): extracting a plurality of key frames from a video stream as input images of a target object (FIG. 2, S200 – S205; FIG. 4, S100; [0006]; [0036]); determining masks, reflection images, and camera parameters of the input images (FIG. 1, Mask, Albedo; FIGS. 2; FIG. 3, $205.4; ; [0025]; “The camera viewpoint … camera intrinsic parameters …”; [0028], “… segmentation mask, the lighting-related auxiliary parameters and the parameters …”; [0032], “… camera viewpoints … auxiliary parameters …”; [0033], “ … segmented foreground …”; [0035], “Auxiliary parameters may include, … flashlight color temperature, and albedo half-texture”); constructing a model corresponding to the target object based on the masks and the camera parameters (FIGS. 1, 3; [0006]; [0008]; [0011]; [0033]); mapping the model based on the reflection images, to obtain a map of the model (FIGS. 1-4; [0026], [0027], “The predicted albedo, normals, environmental shadow maps, and segmentation mask are fused into the relighted 3D portrait 25 according to lighting condition”; [0031]; [0036], “A predicted image (e.g., predicted albedo, normals, environmental shadow maps, and segmentation mask) for the camera viewpoint is obtained … Auxiliary face meshes with corresponding texture mapping are predicted at S110 … for each of the images (or frames) in the captured sequence … The corresponding texture mapping may include a specification of two-dimensional coordinates in the fixed, predefined texture space for every vertex of the mesh …”); and performing illumination restoration on the model based on the map, to obtain an output image of the target object (Abstract; FIGS. 1-4; [0011]; [0020]; [0027]; [0031]).
-Regarding claim 17, Sevastopolskiy discloses a non-transitory computer-readable storage medium storing a plurality of computer- executable instructions that, when executed by a processor of an electronic device, cause the electronic device to perform an image rendering method including (Abstract; FIGS. 1-5): extracting a plurality of key frames from a video stream as input images of a target object (FIG. 2, S200 – S205; FIG. 4, S100; [0006]; [0036]); determining masks, reflection images, and camera parameters of the input images (FIG. 1, Mask, Albedo; FIGS. 2; FIG. 3, $205.4; ; [0025]; “The camera viewpoint … camera intrinsic parameters …”; [0028], “… segmentation mask, the lighting-related auxiliary parameters and the parameters …”; [0032], “… camera viewpoints … auxiliary parameters …”; [0033], “ … segmented foreground …”; [0035], “Auxiliary parameters may include, … flashlight color temperature, and albedo half-texture”); constructing a model corresponding to the target object based on the masks and the camera parameters (FIGS. 1, 3; [0006]; [0008]; [0011]; [0033]); mapping the model based on the reflection images, to obtain a map of the model (FIGS. 1-4; [0026], [0027], “The predicted albedo, normals, environmental shadow maps, and segmentation mask are fused into the relighted 3D portrait 25 according to lighting condition”; [0031]; [0036], “A predicted image (e.g., predicted albedo, normals, environmental shadow maps, and segmentation mask) for the camera viewpoint is obtained … Auxiliary face meshes with corresponding texture mapping are predicted at S110 … for each of the images (or frames) in the captured sequence … The corresponding texture mapping may include a specification of two-dimensional coordinates in the fixed, predefined texture space for every vertex of the mesh …”); and performing illumination restoration on the model based on the map, to obtain an output image of the target object (Abstract; FIGS. 1-4; [0011]; [0020]; [0027]; [0031]).
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) 8, 16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sevastopolskiy et al (US 20220157014 A1), hereinafter Sevastopolskiy in view of Wen et al (US 20250308062 A1), hereinafter Wen.
-Regarding claims 8, 16 and 20, Sevastopolskiy discloses the method of claim 1, the electronic device of claim 9, and the non-transitory computer-readable storage medium of claim 17.
Sevastopolskiy does not disclose wherein the key frames comprise at least one of the following video frames: a video frame whose point cloud has a number of points selected as target feature points higher than a quantity-of-times threshold, each target feature point being a feature point that is repeatedly selected as an inner group in the point cloud; a video frame whose relative motion distance with an adjacent previous video frame is lower than a distance threshold; and a video frame that has a quantity of matching feature points with the adjacent previous video frame higher than a quantity threshold.
In the same field of endeavor, Wen teaches a method for image rendering based on optimized key frame (Wen: Abstract; FIGS. 1-3). Wen further teaches wherein the key frames comprise at least one of the following video frames: a video frame whose point cloud has a number of points selected as target feature points higher than a quantity-of-times threshold, each target feature point being a feature point that is repeatedly selected as an inner group in the point cloud; a video frame whose relative motion distance with an adjacent previous video frame is lower than a distance threshold; and a video frame that has a quantity of matching feature points with the adjacent previous video frame higher than a quantity threshold (Wen: FIGS. 1-2; [0052]; “… number of the target feature points being greater than a first preset number threshold … it is determined that the current frame is a key frame”; [0054]-[0061]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Sevastopolskiy with the teaching of Wen by using optimized key frame in order to improve image rendering efficiency (Wen: [0005]).
Allowable Subject Matter
Claims 2-7, 10-15 and 18-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Izquierdo et al (Modeling arbitrary objects based on geometric surface conformity, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 9, No. 2, pp. 336-352, 1999) hereinafter Izquierdo teaches a method for modeling of arbitrary three-dimensional (3-D) objects by modeling of objects with arbitrary surface shape and generating spatially optimized and temporally consistent models that properly reflect the physical surface characteristics of the objects.
Villegas et al (US 20230037591 A1), hereinafter Villegas teaches a method for generating realistic shading for three-dimensional objects inserted into digital images.
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/XIAO LIU/Primary Examiner, Art Unit 2664
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