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    NEURAL NETWORK MASK GENERATION BASED ON TEMPORAL WINDOWS
    2.
    发明申请
    NEURAL NETWORK MASK GENERATION BASED ON TEMPORAL WINDOWS 有权

    公开(公告)号：US20250088650A1

    公开(公告)日：2025-03-13

    申请号：US18367377

    申请日：2023-09-12

    Applicant: Adobe Inc.

    Inventor： Nikhil Kalra ,  Seoung Wug Oh ,  Nico Alexander Becherer ,  Joon-Young Lee ,  Jimei Yang

    IPC: H04N19/436 ,  G11B27/031 ,  H04N19/119 ,  H04N19/136 ,  H04N19/172 ,  H04N19/85

    Abstract: In one aspect, a processor determines a first set of video frames of a video based on a target video frame. The first set of video frames includes the target video frame, one or more frames of the video preceding the target video frame, and one or more frames of the video subsequent to the target video frame. The first set of video frames includes a sequence of video frames of the video. An encoder neural network executing on the processor encodes the first set of video frames of a video to generate a respective feature vector for each video frame in the first set. A decoder neural network executing on the processor decodes the feature vectors to generate a mask for the target video frame.

    GENERATING SEGMENTATION MASKS FOR OBJECTS IN DIGITAL VIDEOS USING POSE TRACKING DATA
    3.
    发明公开
    GENERATING SEGMENTATION MASKS FOR OBJECTS IN DIGITAL VIDEOS USING POSE TRACKING DATA 审中-公开

    公开(公告)号：US20230196817A1

    公开(公告)日：2023-06-22

    申请号：US17552857

    申请日：2021-12-16

    Applicant: Adobe Inc.

    Inventor： Seoung Wug Oh ,  Miran Heo ,  Joon-Young Lee

    IPC: G06V40/10 ,  G06T7/10 ,  G06T7/70 ,  G06V10/40

    CPC classification number: G06V40/103 ,  G06T7/10 ,  G06T7/70 ,  G06V10/40 ,  G06T2207/20084 ,  G06T2207/30196

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate joint-based segmentation masks for digital objects portrayed in digital videos. In particular, in one or more embodiments, the disclosed systems utilize a video masking model having a pose tracking neural network and a segmentation neural network to generate the joint-based segmentation masks. To illustrate, in some embodiments, the disclosed systems utilize the pose tracking neural network to identify a set of joints of the digital object across the frames of the digital video. The disclosed systems further utilize the segmentation neural network to generate joint-based segmentation masks for the video frames that portray the object using the identified joints. In some cases, the segmentation neural network includes a multi-layer perceptron mixer layer for mixing visual features propagated via convolutional layers.

    VIDEO GENERATION USING FRAME-WISE TOKEN EMBEDDINGS
    4.
    发明申请
    VIDEO GENERATION USING FRAME-WISE TOKEN EMBEDDINGS 有权

    公开(公告)号：US20250119624A1

    公开(公告)日：2025-04-10

    申请号：US18894443

    申请日：2024-09-24

    Applicant: ADOBE INC.

    Inventor： Seoung Wug Oh ,  Mingi Kwon ,  Joon-Young Lee ,  Yang Zhou ,  Difan Liu ,  Haoran Cai ,  Baqiao Liu ,  Feng Liu

    IPC: H04N21/81

    Abstract: A method, apparatus, non-transitory computer readable medium, and system for generating synthetic videos includes obtaining an input prompt describing a video scene. The embodiments then generate a plurality of frame-wise token embeddings corresponding to a sequence of video frames, respectively, based on the input prompt. Subsequently, embodiments generate, using a video generation model, a synthesized video depicting the video scene. The synthesized includes a plurality of images corresponding to the sequence of video frames.

    GENERATING IMPROVED PANOPTIC SEGMENTED DIGITAL IMAGES BASED ON PANOPTIC SEGMENTATION NEURAL NETWORKS THAT UTILIZE EXEMPLAR UNKNOWN OBJECT CLASSES
    7.
    发明申请
    GENERATING IMPROVED PANOPTIC SEGMENTED DIGITAL IMAGES BASED ON PANOPTIC SEGMENTATION NEURAL NETWORKS THAT UTILIZE EXEMPLAR UNKNOWN OBJECT CLASSES 有权

    公开(公告)号：US20220375090A1

    公开(公告)日：2022-11-24

    申请号：US17319979

    申请日：2021-05-13

    Applicant: Adobe Inc.

    Inventor： Jaedong Hwang ,  Seoung Wug Oh ,  Joon-Young Lee

    IPC: G06T7/11 ,  G06K9/62 ,  G06K9/46

    Abstract: This disclosure describes one or more implementations of a panoptic segmentation system that generates panoptic segmented digital images that classify both known and unknown instances of digital images. For example, the panoptic segmentation system builds and utilizes a panoptic segmentation neural network to discover, cluster, and segment new unknown object subclasses for previously unknown object instances. In addition, the panoptic segmentation system can determine additional unknown object instances from additional digital images. Moreover, in some implementations, the panoptic segmentation system utilizes the newly generated unknown object subclasses to refine and tune the panoptic segmentation neural network to improve the detection of unknown object instances in input digital images.

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