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    Point-based neural radiance field for three dimensional scene representation
    21.
    发明授权
    Point-based neural radiance field for three dimensional scene representation 有权

    公开(公告)号：US12073507B2

    公开(公告)日：2024-08-27

    申请号：US17861199

    申请日：2022-07-09

    Applicant: Adobe Inc.

    Inventor： Zexiang Xu ,  Zhixin Shu ,  Sai Bi ,  Qiangeng Xu ,  Kalyan Sunkavalli ,  Julien Philip

    IPC: G06T15/20 ,  G06T15/06 ,  G06T15/80

    CPC classification number: G06T15/205 ,  G06T15/06 ,  G06T15/80 ,  G06T2207/10028

    Abstract: A scene modeling system receives a plurality of input two-dimensional (2D) images corresponding to a plurality of views of an object and a request to display a three-dimensional (3D) scene that includes the object. The scene modeling system generates an output 2D image for a view of the 3D scene by applying a scene representation model to the input 2D images. The scene representation model includes a point cloud generation model configured to generate, based on the input 2D images, a neural point cloud representing the 3D scene. The scene representation model includes a neural point volume rendering model configured to determine, for each pixel of the output image and using the neural point cloud and a volume rendering process, a color value. The scene modeling system transmits, responsive to the request, the output 2D image. Each pixel of the output image includes the respective determined color value.

    GENERATIVE MODEL FOR MULTI-MODALITY OUTPUTS FROM A SINGLE INPUT
    24.
    发明公开
    GENERATIVE MODEL FOR MULTI-MODALITY OUTPUTS FROM A SINGLE INPUT 审中-公开

    公开(公告)号：US20240135672A1

    公开(公告)日：2024-04-25

    申请号：US17971169

    申请日：2022-10-20

    Applicant: Adobe Inc.

    Inventor： Yijun Li ,  Zhixin Shu ,  Zhen Zhu ,  Krishna Kumar Singh

    IPC: G06V10/70 ,  G06N3/04 ,  G06T11/00 ,  G06T15/08

    CPC classification number: G06V10/70 ,  G06N3/0454 ,  G06T11/001 ,  G06T15/08

    Abstract: An image generation system implements a multi-branch GAN to generate images that each express visually similar content in a different modality. A generator portion of the multi-branch GAN includes multiple branches that are each tasked with generating one of the different modalities. A discriminator portion of the multi-branch GAN includes multiple fidelity discriminators, one for each of the generator branches, and a consistency discriminator, which constrains the outputs generated by the different generator branches to appear visually similar to one another. During training, outputs from each of the fidelity discriminators and the consistency discriminator are used to compute a non-saturating GAN loss. The non-saturating GAN loss is used to refine parameters of the multi-branch GAN during training until model convergence. The trained multi-branch GAN generates multiple images from a single input, where each of the multiple images depicts visually similar content expressed in a different modality.

    COMPRESSING GENERATIVE ADVERSARIAL NEURAL NETWORKS
    25.
    发明申请
    COMPRESSING GENERATIVE ADVERSARIAL NEURAL NETWORKS 有权

    公开(公告)号：US20220222532A1

    公开(公告)日：2022-07-14

    申请号：US17147912

    申请日：2021-01-13

    Applicant: Adobe Inc.

    Inventor： Zhixin Shu ,  Zhe Lin ,  Yuchen Liu ,  Yijun Li

    IPC: G06N3/08 ,  G06T5/00 ,  G06T5/40 ,  G06T7/194

    Abstract: This disclosure describes one or more embodiments of systems, non-transitory computer-readable media, and methods that utilize channel pruning and knowledge distillation to generate a compact noise-to-image GAN. For example, the disclosed systems prune less informative channels via outgoing channel weights of the GAN. In some implementations, the disclosed systems further utilize content-aware pruning by utilizing a differentiable loss between an image generated by the GAN and a modified version of the image to identify sensitive channels within the GAN during channel pruning. In some embodiments, the disclosed systems utilize knowledge distillation to learn parameters for the pruned GAN to mimic a full-size GAN. In certain implementations, the disclosed systems utilize content-aware knowledge distillation by applying content masks on images generated by both the pruned GAN and its full-size counterpart to obtain knowledge distillation losses between the images for use in learning the parameters for the pruned GAN.

    Neural face editing with intrinsic image disentangling
    26.
    发明授权
    Neural face editing with intrinsic image disentangling 有权

    公开(公告)号：US10565758B2

    公开(公告)日：2020-02-18

    申请号：US15622711

    申请日：2017-06-14

    Applicant: Adobe Inc.

    Inventor： Sunil Hadap ,  Elya Shechtman ,  Zhixin Shu ,  Kalyan Sunkavalli ,  Mehmet Yumer

    IPC: G06K9/62 ,  G06T11/60 ,  G06T5/00

    Abstract: Techniques are disclosed for performing manipulation of facial images using an artificial neural network. A facial rendering and generation network and method learns one or more compact, meaningful manifolds of facial appearance, by disentanglement of a facial image into intrinsic facial properties, and enables facial edits by traversing paths of such manifold(s). The facial rendering and generation network is able to handle a much wider range of manipulations including changes to, for example, viewpoint, lighting, expression, and even higher-level attributes like facial hair and age—aspects that cannot be represented using previous models.

    GENERATING THREE-DIMENSIONAL LOOPING ANIMATIONS FROM STILL IMAGES
    27.
    发明申请
    GENERATING THREE-DIMENSIONAL LOOPING ANIMATIONS FROM STILL IMAGES 有权

    公开(公告)号：US20240428491A1

    公开(公告)日：2024-12-26

    申请号：US18340445

    申请日：2023-06-23

    Applicant: Adobe Inc.

    Inventor： Jae Shin Yoon ,  Duygu Ceylan Aksit ,  Yangtuanfeng Wang ,  Jingwan Lu ,  Jimei Yang ,  Zhixin Shu ,  Chengan He ,  Yi Zhou ,  Jun Saito ,  James Zachary

    IPC: G06T13/40

    Abstract: The present disclosure relates to a system that utilizes neural networks to generate looping animations from still images. The system fits a 3D model to a pose of a person in a digital image. The system receives a 3D animation sequence that transitions between a starting pose and an ending pose. The system generates, utilizing an animation transition neural network, first and second 3D animation transition sequences that respectively transition between the pose of the person and the starting pose and between the ending pose and the pose of the person. The system modifies each of the 3D animation sequence, the first 3D animation transition sequence, and the second 3D animation transition sequence by applying a texture map. The system generates a looping 3D animation by combining the modified 3D animation sequence, the modified first 3D animation transition sequence, and the modified second 3D animation transition sequence.

    DEFORMABLE NEURAL RADIANCE FIELD FOR EDITING FACIAL POSE AND FACIAL EXPRESSION IN NEURAL 3D SCENES
    28.
    发明公开
    DEFORMABLE NEURAL RADIANCE FIELD FOR EDITING FACIAL POSE AND FACIAL EXPRESSION IN NEURAL 3D SCENES 审中-公开

    公开(公告)号：US20240062495A1

    公开(公告)日：2024-02-22

    申请号：US17892097

    申请日：2022-08-21

    Applicant: Adobe Inc.

    Inventor： Zhixin Shu ,  Zexiang Xu ,  Shahrukh Athar ,  Kalyan Sunkavalli ,  Elya Shechtman

    IPC: G06T19/20 ,  G06T17/00

    CPC classification number: G06T19/20 ,  G06T17/00 ,  G06T2200/08 ,  G06T2219/2021

    Abstract: A scene modeling system receives a video including a plurality of frames corresponding to views of an object and a request to display an editable three-dimensional (3D) scene that corresponds to a particular frame of the plurality of frames. The scene modeling system applies a scene representation model to the particular frame, and includes a deformation model configured to generate, for each pixel of the particular frame based on a pose and an expression of the object, a deformation point using a 3D morphable model (3DMM) guided deformation field. The scene representation model includes a color model configured to determine, for the deformation point, color and volume density values. The scene modeling system receives a modification to one or more of the pose or the expression of the object including a modification to a location of the deformation point and renders an updated video based on the received modification.

    ATTRIBUTE DECORRELATION TECHNIQUES FOR IMAGE EDITING
    30.
    发明申请
    ATTRIBUTE DECORRELATION TECHNIQUES FOR IMAGE EDITING 有权

    公开(公告)号：US20220122232A1

    公开(公告)日：2022-04-21

    申请号：US17468476

    申请日：2021-09-07

    Applicant: Adobe Inc.

    Inventor： Wei-An Lin ,  Baldo Faieta ,  Cameron Smith ,  Elya Shechtman ,  Jingwan Lu ,  Jun-Yan Zhu ,  Niloy Mitra ,  Ratheesh Kalarot ,  Richard Zhang ,  Shabnam Ghadar ,  Zhixin Shu

    IPC: G06T5/00 ,  G06T5/20 ,  G06N3/08

    Abstract: Systems and methods generate a filtering function for editing an image with reduced attribute correlation. An image editing system groups training data into bins according to a distribution of a target attribute. For each bin, the system samples a subset of the training data based on a pre-determined target distribution of a set of additional attributes in the training data. The system identifies a direction in the sampled training data corresponding to the distribution of the target attribute to generate a filtering vector for modifying the target attribute in an input image, obtains a latent space representation of an input image, applies the filtering vector to the latent space representation of the input image to generate a filtered latent space representation of the input image, and provides the filtered latent space representation as input to a neural network to generate an output image with a modification to the target attribute.

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