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公开(公告)号：US12266039B2
公开(公告)日：2025-04-01
申请号：US17985579
申请日：2022-11-11
Applicant: Adobe Inc.
Inventor： Valentin Deschaintre , Yiwei Hu , Paul Guerrero , Milos Hasan
Abstract: Certain aspects and features of this disclosure relate to rendering images using target-augmented material maps. In one example, a graphics imaging application is loaded with a scene and an input material map, as well as a file for a target image. A stored, material generation prior is accessed by the graphics imaging application. This prior, as an example, is based on a pre-trained, generative adversarial network (GAN). An input material appearance from the input material map is encoded to produce a projected latent vector. The value for the projected latent vector is optimized to produce the material map that is used to render the scene, producing a material map augmented by a realistic target material appearance.
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公开(公告)号：US20240404244A1
公开(公告)日：2024-12-05
申请号：US18329385
申请日：2023-06-05
Applicant: Adobe Inc.
Inventor： Valentin Mathieu Deschaintre , Yiwei Hu , Paul Guerrero , Milos Hasan
Abstract: Conditional procedural model generation techniques are described that enable generation of procedural models that are usable to recreate a visual appearance of an input image. A content processing system, for instance, receives a training dataset that includes a plurality of training pairs. The content processing system trains a machine learning model to generate procedural models based on input images. The content processing system then receives an input image that has a particular visual appearance. The content processing system leverages the trained machine learning model to generate a procedural model that is usable to recreate the particular visual appearance of the input digital image.
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公开(公告)号：US12125138B2
公开(公告)日：2024-10-22
申请号：US17864901
申请日：2022-07-14
Applicant: Adobe Inc.
Inventor： Valentin Deschaintre , Yiwei Hu , Paul Guerrero , Milos Hasan
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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公开(公告)号：US20240161362A1
公开(公告)日：2024-05-16
申请号：US17985579
申请日：2022-11-11
Applicant: Adobe Inc.
Inventor： Valentin Deschaintre , Yiwei Hu , Paul Guerrero , Milos Hasan
CPC classification number: G06T11/60 , G06T3/40 , G06T9/00 , G06V10/761
Abstract: Certain aspects and features of this disclosure relate to rendering images using target-augmented material maps. In one example, a graphics imaging application is loaded with a scene and an input material map, as well as a file for a target image. A stored, material generation prior is accessed by the graphics imaging application. This prior, as an example, is based on a pre-trained, generative adversarial network (GAN). An input material appearance from the input material map is encoded to produce a projected latent vector. The value for the projected latent vector is optimized to produce the material map that is used to render the scene, producing a material map augmented by a realistic target material appearance.
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