NODE GRAPH OPTIMIZATION USING DIFFERENTIABLE PROXIES

    公开(公告)号:US20240020916A1

    公开(公告)日:2024-01-18

    申请号:US17864901

    申请日:2022-07-14

    Applicant: Adobe Inc.

    CPC classification number: G06T17/00 G06T15/04 G06V10/82

    Abstract: Embodiments are disclosed for optimizing a material graph for replicating a material of the target image. Embodiments include receiving a target image and a material graph to be optimized for replicating a material of the target image. Embodiments include identifying a non-differentiable node of the material graph, the non-differentiable node including a set of input parameters. Embodiments include selecting a differentiable proxy from a library of the selected differentiable proxy is trained to replicate an output of the identified non-differentiable node. Embodiments include generating an optimized input parameters for the identified non-differentiable node using the corresponding trained neural network and the target image. Embodiments include replacing the set of input parameters of the non-differentiable node of the material graph with the optimized input parameters. Embodiments include generating an output material by the material graph to represent the target image using the optimized input parameters for the non-differentiable node.

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