DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
2. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
3. The Information Disclosure Statements filed 11 March 2025 and 16 April 2025 have been fully considered by Examiner. Annotated copies are included herewith.
Claim Rejections - 35 USC § 101
4. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
5. Claims 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to a “computer-readable medium using computer-executable instructions” which includes not only statutory embodiments, such as a non-transitory computer-readable medium or a computer-readable device, but also non-statutory embodiments, such as a computer-readable electromagnetic signal medium encoded with the computer-readable instructions. See MPEP § 2106.03(I). Therefore, claims 15-20 are rejected under 35 U.S.C. § 101.
Allowable Subject Matter
6. Claims 1-14 are allowed. Claims 15-20, while not allowable, distinguish over the prior art.
Claim 1 recites a “computer-implemented method of generating fullbody animatable avatar of a person from a single image of the person, the method comprising:
“obtaining an image of a person body and a parametric body model defined by pose parameters and shape parameters of the person body in the image, and by camera parameters used when capturing the image;
“defining, based on the parametric body model, a texturing function including a mapping between each pixel corresponding to a part of the person body shown in the image and corresponding texture coordinates in a texture space, and corresponding texture coordinates in the texture space for a part of the person body not shown in the image;
“sampling RGB texture of the person body based on the mapping and obtaining a map of sampled pixels, wherein the RGB texture includes:
“for each pixel corresponding to a part of the person body shown in the image, a corresponding pixel value, and
“one or more unshown texture regions corresponding to the texture coordinates for the part of the person body not shown in the image;
“passing the image of the person body through a trained encoder-generator network to generate texture of the person body shown in the image;
“concatenating the RGB texture, the map of sampled pixels, and the generated texture to obtain neural texture;
“inpainting unshown texture regions of the neural texture by a trained diffusion-based inpainting model; and
“translating a rasterized image of a fullbody avatar of the person body in a different pose by a trained neural renderer into a rendered image of the fullbody avatar of the person body in the different pose,
“wherein the rasterized image is obtained based on the inpainted neural texture and the mapping included in the texturing function, in which wherein the parametric body model is modified based on target pose parameters or target camera parameters, and
“wherein the target pose parameters or the target camera parameters correspond to the different pose of the fullbody avatar of the person body.”
While there is related prior art, Examiner has not discovered any prior art which fully teaches claim 1, either singly or in an obvious combination. Related prior art includes:
A. A. Raj, J. Tanke, J. Hays, M. Vo, C. Stoll and C. Lassner, “ANR: Articulated Neural Rendering for Virtual Avatars,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021, pp. 3721-3730, doi: 10.1109/CVPR46437.2021.00372, which teaches Articulated Neural Rendering (ANR), a Deferred Neural Rendering (DNR) for creating, animating, and rendering virtual human avatars, accounting for geometric misalignment and pose-dependent deformation to provide better temporal stability, level of detail and plausibility.
B. A. Shysheya et al., “Textured Neural Avatars,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 2382-2392, doi: 10.1109/CVPR.2019.00249, which teaches a system for learning full body neural avatars, using deep networks that produce full body renderings of a person for varying body and camera poses, and generating images of humans using image-to-image translation.
C. A. Grigorev et al., “StylePeople: A Generative Model of Fullbody Human Avatars,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021, pp. 5147-5156, doi: 10.1109/CVPR46437.2021.00511, which teaches modeling full-body human avatars with clothing and hair, combining a parametric mesh-based body model with a neural texture.
D. Kanazawa et al., US-2025/0086760-A1, which teaches machine-learning models and contextual attention data to provide more realistic and efficient data augmentation, including inpainting which uses a machine-learning model to generate predicted contextual attention data and blend the predicted contextual attention data with obtained contextual attention data to determine replacement data for augmenting an image to replace one or more occlusions.
E. Zheng et al., US-2025/0054116-A1, which teaches generating in-painted digital images by generating replacement pixels for replacement regions.
F. Sarafianos et al., US-2024/0078745-A1, which teaches mapping a body surface at multiple virtual viewpoints, including texture and in-painting, using a machine-learning model.
G. Petrangeli et al., US-2023/0162330-A1, which teaches mapping an input image to modify a target region using a neural network.
H. Garg et al., US-2023/0046431-A1, which teaches generating 3D virtual objects from 2D garment images using 3D training models.
I. Vo et al., US-2022/0036626-A1, which teaches generate 3D avatars of a person with neural networks using multiple poses, and generating rendered neural textures based on mapping between the 3D geometry visible from the viewing direction and the neural texture.
J. Shysheya et al., US-2021/0358197-A1, which teaches textured neural avatars.
None of the prior art cited above, nor any other prior art discovered by Examiner, fully teaches claim 1, either singly or in an obvious combination. Therefore, claim 1 distinguishes over the prior art.
Claims 14 and 15 each distinguish over the prior art for the reasons set forth above with respect to claim 1. Claims 2-13 and 16-20 each distinguish over the prior art at least due to their respective dependencies.
Claims 1-14 each distinguish over the prior art, and there are no outstanding grounds of rejection or objection for claims 1-14. Accordingly, claims 1-14 are allowed. Claims 15-20 each distinguish over the prior art, but there are rejections under 35 U.S.C. § 101 outstanding, as set forth above.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James A Thompson whose telephone number is (571)272-7441. The examiner can normally be reached M-F 8am-6pm.
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/JAMES A THOMPSON/Primary Examiner, Art Unit 2615
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