INTERACTIVE COLOR PALETTE INTERFACE FOR DIGITAL PAINTING

    公开(公告)号:US20180322661A1

    公开(公告)日:2018-11-08

    申请号:US15589223

    申请日:2017-05-08

    CPC classification number: G06T11/001 G06F3/0488 G06F3/04883 G06T2200/24

    Abstract: An interactive palette interface includes a color picker for digital paint applications. A user can create, modify and select colors for creating digital artwork using the interactive palette interface. The interactive palette interface includes a mixing dish in which colors can be added, removed and rearranged to blend together to create gradients and gamuts. The mixing dish is a digital simulation of a physical palette on which an artist adds and mixes various colors of paint before applying the paint to the artwork. Color blobs, which are logical groups of pixels in the mixing dish, can be spatially rearranged and scaled by a user to create and explore different combinations of colors. The color, position and size of each blob influences the color of other pixels in the mixing dish. Edits to the mixing dish are non-destructive, and an infinite history of color combinations is preserved.

    Stroke Operation Prediction for Three-Dimensional Digital Content

    公开(公告)号:US20180239434A1

    公开(公告)日:2018-08-23

    申请号:US15438276

    申请日:2017-02-21

    Abstract: Stroke operation prediction techniques and systems for three-dimensional digital content are described. In one example, stroke operation data is received that describes a stroke operation input via a user interface as part of the three-dimensional digital content. A cycle is generated that defines a closed path within the three-dimensional digital content based on the input stroke operation and at least one other stroke operation in the user interface. A surface is constructed based on the generated cycle. A predicted stroke operation is generated based at least in part on the constructed surface. The predicted stroke operation is then output in real time in the user interface as part of the three-dimensional digital content as the stroke operation data is received.

    INTERACTIVE GENERATION OF PROCEDURAL ORNAMENTS

    公开(公告)号:US20180101972A1

    公开(公告)日:2018-04-12

    申请号:US15288999

    申请日:2016-10-07

    CPC classification number: G06T11/60 G06T11/001

    Abstract: A procedural model enables a user to configure a global space organization function for the generation of decorative ornaments. The user provides data to seed the generation of the ornaments, as well as localized interactive edits to the generated ornaments. The procedural model iteratively places decorative elements at a subset of locations within an ornament area (or domain) based on generalized placement functions employed by the global space organization function. As such, the user is enabled to interactively generate and edit decorative ornaments via configuring the global space organization function and employing editing tools. Such functionality significantly decreases the effort typically required to generate ornate ornaments, while retaining control of the aesthetic organization and structure of the ornament. The generalized placement functions and heuristics of the global space organization function enable such control.

    RECOMMENDING PATTERN DESIGNS FOR OBJECTS USING A SEQUENCE-BASED MACHINE-LEARNING MODEL

    公开(公告)号:US20170169340A1

    公开(公告)日:2017-06-15

    申请号:US14968870

    申请日:2015-12-14

    Abstract: Methods and systems for aiding users in generating object pattern designs with increased speed. In particular, one or more embodiments train a sequence-based machine-learning model using training objects, each training object including a plurality of regions with a plurality of design elements. One or more embodiments identify a plurality regions of an object with a first region adjacent a second region. One or more embodiments receive a user selection of a design element for populating the first region with a first design element from a plurality of design elements. One or more embodiments identify a second design element from the plurality of design elements based on the first design element using the trained sequence-based machine-learning model. One or more embodiments also populate the second region with one or more instances of the second design element.

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