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    SEMANTICALLY-AWARE IMAGE EXTRAPOLATION
    1.
    发明公开
    SEMANTICALLY-AWARE IMAGE EXTRAPOLATION 审中-公开

    公开(公告)号：US20230169632A1

    公开(公告)日：2023-06-01

    申请号：US17521503

    申请日：2021-11-08

    Applicant: Adobe Inc.

    Inventor： Kuldeep Kulkarni ,  Soumya Dash ,  Hrituraj Singh ,  Bholeshwar Khurana ,  Aniruddha Mahapatra ,  Abhishek Bhatia

    IPC: G06T5/50 ,  G06T7/181

    CPC classification number: G06T5/50 ,  G06T7/181

    Abstract: Certain aspects and features of this disclosure relate to semantically-aware image extrapolation. In one example, an input image is segmented to produce an input segmentation map of object instances in the input image. An object generation network is used to generate an extrapolated semantic label map for an extrapolated image. The extrapolated semantic label map includes instances in the original image and instances that will appear in an outpainted region of the extrapolated image. A panoptic label map is derived from coordinates of output instances in the extrapolated image and used to identify partial instances and boundaries. Instance-aware context normalization is used to apply one or more characteristics from the input image to the outpainted region to maintain semantic continuity. The extrapolated image includes the original image and the outpainted region and can be rendered or stored for future use.

    Machine learning based controllable animation of still images
    2.
    发明授权
    Machine learning based controllable animation of still images 有权

    公开(公告)号：US12282992B2

    公开(公告)日：2025-04-22

    申请号：US17856362

    申请日：2022-07-01

    Applicant: ADOBE INC.

    Inventor： Kuldeep Kulkarni ,  Aniruddha Mahapatra

    IPC: G06T13/80 ,  G06T3/18 ,  G06T7/215

    Abstract: Systems and methods for machine learning based controllable animation of still images is provided. In one embodiment, a still image including a fluid element is obtained. Using a flow refinement machine learning model, a refined dense optical flow is generated for the still image based on a selection mask that includes the fluid element and a dense optical flow generated from a motion hint that indicates a direction of animation. The refined dense optical flow indicates a pattern of apparent motion for the at least one fluid element. Thereafter, a plurality of video frames is generated by projecting a plurality of pixels of the still image using the refined dense optical flow.

    Object Insertion via Scene Graph
    4.
    发明公开
    Object Insertion via Scene Graph 审中-公开

    公开(公告)号：US20240202876A1

    公开(公告)日：2024-06-20

    申请号：US18067989

    申请日：2022-12-19

    Applicant: Adobe Inc.

    Inventor： Tripti Shukla ,  Kuldeep Kulkarni ,  Paridhi Maheshwari

    IPC: G06T5/50 ,  G06V10/82 ,  G06V20/70

    CPC classification number: G06T5/50 ,  G06V10/82 ,  G06V20/70 ,  G06T2207/20221

    Abstract: Techniques are described for object insertion via scene graph. In implementations, given an input image and a region of the image where a new object is to be inserted, the input image is converted to an intermediate scene graph space. In the intermediate scene graph space, graph convolutional networks are leveraged to expand the scene graph by predicting the identity and relationships of a new object to be inserted, taking into account existing objects in the input image. The expanded scene graph and the input image are then processed by an image generator to insert a predicted visual object into the input image to produce an output image.

    CREATING CINEMAGRAPHS FROM A SINGLE IMAGE
    7.
    发明申请
    CREATING CINEMAGRAPHS FROM A SINGLE IMAGE 有权

    公开(公告)号：US20240404155A1

    公开(公告)日：2024-12-05

    申请号：US18325645

    申请日：2023-05-30

    Applicant: Adobe Inc.

    Inventor： Duygu Ceylan Aksit ,  Hugo Bertiche Argila ,  Niloy Jyoti Mitra ,  Kuldeep Kulkarni ,  Chun Hao Huang ,  Yangtuanfeng Wang

    IPC: G06T13/00 ,  G06N3/045 ,  G06T11/60

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilizes neural networks to generate cinemagraphs from single RGB images. For example, the cyclic animation system includes a cyclic animation neural network trained with synthetic data, wherein different wind effects can be replicated using physically based simulations to create cyclic videos more efficiently. More specifically, the cyclic animation system generalizes a solution by operating in the gradient domain and using surface normal maps. Because normal maps are invariant to appearance (color, texture, illumination, etc.), the gap between synthetic and real data distribution in the normal map space is smaller than in the RGB space. The cyclic animation system performs a reshading approach that synthesizes RGB pixels from the original image and the animated normal maps to create plausible changes to the real image to create the cinemagraph.

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