DIGITAL IMAGE DECALING
    1.
    发明申请

    公开(公告)号:US20240378809A1

    公开(公告)日:2024-11-14

    申请号:US18316490

    申请日:2023-05-12

    Applicant: Adobe Inc.

    Abstract: Decal application techniques as implemented by a computing device are described to perform decaling of a digital image. In one example, learned features of a digital image using machine learning are used by a computing device as a basis to predict the surface geometry of an object in the digital image. Once the surface geometry of the object is predicted, machine learning techniques are then used by the computing device to configure an overlay object to be applied onto the digital image according to the predicted surface geometry of the overlaid object.

    Image modification using detected symmetry

    公开(公告)号:US11551388B2

    公开(公告)日:2023-01-10

    申请号:US16794908

    申请日:2020-02-19

    Applicant: Adobe Inc.

    Abstract: Image modification using detected symmetry is described. In example implementations, an image modification module detects multiple local symmetries in an original image by discovering repeated correspondences that are each related by a transformation. The transformation can include a translation, a rotation, a reflection, a scaling, or a combination thereof. Each repeated correspondence includes three patches that are similar to one another and are respectively defined by three pixels of the original image. The image modification module generates a global symmetry of the original image by analyzing an applicability to the multiple local symmetries of multiple candidate homographies contributed by the multiple local symmetries. The image modification module associates individual pixels of the original image with a global symmetry indicator to produce a global symmetry association map. The image modification module produces a manipulated image by manipulating the original image under global symmetry constraints imposed by the global symmetry association map.

    Multi-layer Lighting Source With Textured Lighting Gel Layer

    公开(公告)号:US20220130087A1

    公开(公告)日:2022-04-28

    申请号:US17082378

    申请日:2020-10-28

    Applicant: Adobe Inc.

    Abstract: A multi-layer light source includes an emissive layer and a textured lighting gel layer, the lighting gel layer being situated between the emissive layer and a 2D canvas or a 3D object. User inputs controlling the multi-layer light source are received, these user inputs being provided with the user interacting with the 2D canvas without switching to editing in 3D space. The multi-layer light source is configured based on the user inputs and, based on the configuration, emission of light rays from the multi-layer light source is determined. Areas of shadows cast by 3D objects are also determined. An image generation system determines, a color of a location (e.g., a pixel) on the 2D canvas or the 3D object that a light ray intersects based on the color that is in the lighting gel layer that the light ray passes through.

    Visualizing vector graphics in three-dimensional scenes

    公开(公告)号:US12229892B2

    公开(公告)日:2025-02-18

    申请号:US18157940

    申请日:2023-01-23

    Applicant: Adobe Inc.

    Abstract: In implementations of systems for visualizing vector graphics in three-dimensional scenes, a computing device implements a projection system to receive input data describing a digital image depicting a three-dimensional scene and a vector graphic to be projected into the three-dimensional scene. The projection system generates a depth image by estimating disparity values for pixels of the digital image. A three-dimensional mesh is computed that approximates the three-dimensional scene based on the depth image. The projection system projects the vector graphic onto the digital image by transforming the vector graphic based on the three-dimensional mesh.

    RENDERING IMAGES FROM DEEPLY LEARNED RAYTRACING PARAMETERS

    公开(公告)号:US20200312009A1

    公开(公告)日:2020-10-01

    申请号:US16368548

    申请日:2019-03-28

    Applicant: ADOBE INC.

    Abstract: Images are rendered from deeply learned raytracing parameters. Active learning, via a machine learning (ML) model (e.g., implemented by a deep neural network), is used to automatically determine, infer, and/or predict optimized, or at least somewhat optimized, values for parameters used in raytracing methods. Utilizing deep learning to determine optimized, or at least somewhat optimized, values for raytracing parameters is in contrast to conventional methods, which require users to rely of heuristics for parameter value setting. In various embodiments, one or more parameters regarding the termination and splitting of traced light paths in stochastic-based (e.g., Monte Carlo) raytracing are determined via active learning. In some embodiments, one or more parameters regarding the sampling rate of shadow rays are also determined.

    Image Modification Using Detected Symmetry
    7.
    发明申请

    公开(公告)号:US20200184697A1

    公开(公告)日:2020-06-11

    申请号:US16794908

    申请日:2020-02-19

    Applicant: Adobe Inc.

    Abstract: Image modification using detected symmetry is described. In example implementations, an image modification module detects multiple local symmetries in an original image by discovering repeated correspondences that are each related by a transformation. The transformation can include a translation, a rotation, a reflection, a scaling, or a combination thereof. Each repeated correspondence includes three patches that are similar to one another and are respectively defined by three pixels of the original image. The image modification module generates a global symmetry of the original image by analyzing an applicability to the multiple local symmetries of multiple candidate homographies contributed by the multiple local symmetries. The image modification module associates individual pixels of the original image with a global symmetry indicator to produce a global symmetry association map. The image modification module produces a manipulated image by manipulating the original image under global symmetry constraints imposed by the global symmetry association map.

    Generative Shape Creation and Editing

    公开(公告)号:US20210264649A1

    公开(公告)日:2021-08-26

    申请号:US17317246

    申请日:2021-05-11

    Applicant: Adobe Inc.

    Abstract: Generative shape creation and editing is leveraged in a digital medium environment. An object editor system represents a set of training shapes as sets of visual elements known as “handles,” and converts sets of handles into signed distance field (SDF) representations. A handle processor model is then trained using the SDF representations to enable the handle processor model to generate new shapes that reflect salient visual features of the training shapes. The trained handle processor model, for instance, generates new sets of handles based on salient visual features learned from the training handle set. Thus, utilizing the described techniques, accurate characterizations of a set of shapes can be learned and used to generate new shapes. Further, generated shapes can be edited and transformed in different ways.

    Drawing curves in space guided by 3-D objects

    公开(公告)号:US11069099B2

    公开(公告)日:2021-07-20

    申请号:US16855328

    申请日:2020-04-22

    Applicant: Adobe Inc.

    Abstract: Various embodiments enable curves to be drawn around 3-D objects by intelligently determining or inferring how the curve flows in the space around the outside of the 3-D object. The various embodiments enable such curves to be drawn without having to constantly rotate the 3-D object. In at least some embodiments, curve flow is inferred by employing a vertex position discovery process, a path discovery process, and a final curve construction process.

    Image modification using detected symmetry

    公开(公告)号:US10573040B2

    公开(公告)日:2020-02-25

    申请号:US15346638

    申请日:2016-11-08

    Applicant: Adobe Inc.

    Abstract: Image modification using detected symmetry is described. In example implementations, an image modification module detects multiple local symmetries in an original image by discovering repeated correspondences that are each related by a transformation. The transformation can include a translation, a rotation, a reflection, a scaling, or a combination thereof. Each repeated correspondence includes three patches that are similar to one another and are respectively defined by three pixels of the original image. The image modification module generates a global symmetry of the original image by analyzing an applicability to the multiple local symmetries of multiple candidate homographies contributed by the multiple local symmetries. The image modification module associates individual pixels of the original image with a global symmetry indicator to produce a global symmetry association map. The image modification module produces a manipulated image by manipulating the original image under global symmetry constraints imposed by the global symmetry association map.

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