Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that provide a differentiable tiling system that generates aesthetically plausible, periodic, and tile-able non-square imagery using machine learning and a text-guided, fully automatic generative approach. Namely, given a textual description of the object and a symmetry pattern of the 2D plane, the system produces a textured 2D mesh which visually resembles the textual description, adheres to the geometric rules which ensure it can be used to tile the plane, and contains only the foreground object. Indeed, the disclosed systems generate a plausible textured 2D triangular mesh that visually matches the textual input and optimizes both the texture and the shape of the mesh and satisfy an overlap condition and a tile-able condition. Using the described methods, the differentiable tiling system generates the mesh such that the edges and the vertices align between repeatable instances of the mesh.

Abstract: In implementation of techniques for progressively generating fine polygon meshes, a computing device implements a mesh progression system to receive a coarse polygon mesh. The mesh progression system generates a fine polygon mesh that has a higher level of resolution than the coarse polygon mesh by decoding the coarse polygon mesh using a machine learning model. The mesh progression system then receives additional data describing a residual feature of a polygon mesh. Based on the additional data, the mesh progression system generates an adjusted fine polygon mesh that has a higher level of resolution than the fine polygon mesh.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing one or more neural networks to recursively subdivide a three-dimensional mesh according to local geometries of vertices in the three-dimensional mesh. For example, the disclosed system can determine a local geometry (e.g., a one-ring neighborhood of half-flaps) for each vertex in a three-dimensional mesh. For each subdivision iteration, the disclosed system can then utilize a neural network to determine displacement coordinates for existing vertices in the three-dimensional mesh and coordinates for new vertices added to edges between the existing vertices in the three-dimensional mesh in accordance with the local geometries of the existing vertices. Furthermore, the disclosed system can generate a subdivided three-dimensional mesh based on the determined displacement coordinates for the existing vertices and the determined coordinates for the new vertices.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed that utilizes machine learning models for patch retrieval and deformation in completing three-dimensional digital shapes. In particular, in one or more implementations the disclosed systems utilize a machine learning model to predict a coarse completion shape from an incomplete 3D digital shape. The disclosed systems sample coarse 3D patches from the coarse 3D digital shape and learn a shape distance function to retrieve detailed 3D shape patches in the input shape. Moreover, the disclosed systems learn a deformation for each retrieved patch and blending weights to integrate the retrieved patches into a continuous surface.

Abstract: Certain aspects and features of this disclosure relate to neural network based 3D object surface mapping. In one example, a first representation of a first surface of a first 3D object and a second representation of a second surface of a second 3D object are produced. A surface mapping function is generated for mapping the first surface to the second surface. The surface mapping function is defined the representations and by a neural network model configured to map a first 2D representation of the first surface to a second 2D representation of the second surface. One or more features of the a first 3D mesh on the first surface can be applied to a second 3D mesh on the second surface using the surface mapping function to produce a modified second surface, which can be rendered through a user interface.

Abstract: Techniques for determining a swept volume of an object moving along a trajectory in a 3D space are disclosed. In some examples, a computer graphics application accesses a representation of the object, such as the signed distance field (SDF), and the trajectory information describing the movement path in the 3D space over a time period. The 3D space is represented using a grid of voxels each having multiple vertices. The computer graphics application determines the swept volume of the object in the 3D space by evaluating a subset of the grid of voxels (e.g., the voxels surrounding the surface of the swept volume). The number of voxels in the subset of voxels is less than the number of voxels in the grid of voxels. The computer graphics application further generates a representation of the swept volume surface for output.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating digital chain pull paintings in digital images. The disclosed system digitally animates a chain pull painting from a digital drawing path by determining a plurality of digital bead points along the digital drawing path. In response to a movement of one of the digital bead points from a first position to a second position (e.g., based on a pull input performed at a selected digital bead point), the disclosed system determines updated positions of one or more digital bead points along the path. The disclosed system also generates one or more strokes in the digital image from previous positions of the digital bead points to the updated positions of the digital bead points.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing one or more neural networks to recursively subdivide a three-dimensional mesh according to local geometries of vertices in the three-dimensional mesh. For example, the disclosed system can determine a local geometry (e.g., a one-ring neighborhood of half-flaps) for each vertex in a three-dimensional mesh. For each subdivision iteration, the disclosed system can then utilize a neural network to determine displacement coordinates for existing vertices in the three-dimensional mesh and coordinates for new vertices added to edges between the existing vertices in the three-dimensional mesh in accordance with the local geometries of the existing vertices. Furthermore, the disclosed system can generate a subdivided three-dimensional mesh based on the determined displacement coordinates for the existing vertices and the determined coordinates for the new vertices.

Abstract: Three-dimensional (3D) mesh segmentation techniques are described. In one example, a geometry segmentation system determines a vertex direction for each vertex in a 3D mesh. A segment generation module is then employed to generate segments (e.g., as developable geometries) from the 3D mesh. To do so, a vertex selection module selects an initial vertex having an associated vertex direction. A face identification module then identifies a face in the 3D mesh using that initial vertex and at least one other vertex. A segment determination module compares the vertex direction associated with the initial vertex with a normal determined for the face. If the vertex direction is orthogonal to the normal (e.g., within a threshold amount), the face is added to the segment, and sets another one of the vertices of the face as the initial vertex and the process repeats.

Abstract: Embodiments of the present invention are directed towards intuitive editing of three-dimensional models. In embodiments, salient geometric features associated with a three-dimensional model defining an object are identified. Thereafter, feature attributes associated with the salient geometric features are identified. A feature set including a plurality of salient geometric features related to one another is generated based on the determined feature attributes (e.g., properties, relationships, distances). An editing handle can then be generated and displayed for the feature set enabling each of the salient geometric features within the feature set to be edited in accordance with a manipulation of the editing handle. The editing handle can be displayed in association with one of the salient geometric features of the feature set.

Abstract: Certain aspects and features of this disclosure relate to neural network based 3D object surface mapping. In one example, a first representation of a first surface of a first 3D object and a second representation of a second surface of a second 3D object are produced. A surface mapping function is generated for mapping the first surface to the second surface. The surface mapping function is defined the representations and by a neural network model configured to map a first 2D representation of the first surface to a second 2D representation of the second surface. One or more features of the a first 3D mesh on the first surface can be applied to a second 3D mesh on the second surface using the surface mapping function to produce a modified second surface, which can be rendered through a user interface.

Abstract: Techniques for determining a swept volume of an object moving along a trajectory in a 3D space are disclosed. In some examples, a computer graphics application accesses a representation of the object, such as the signed distance field (SDF), and the trajectory information describing the movement path in the 3D space over a time period. The 3D space is represented using a grid of voxels each having multiple vertices. The computer graphics application determines the swept volume of the object in the 3D space by evaluating a subset of the grid of voxels (e.g., the voxels surrounding the surface of the swept volume). The number of voxels in the subset of voxels is less than the number of voxels in the grid of voxels. The computer graphics application further generates a representation of the swept volume surface for output.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing one or more neural networks to recursively subdivide a three-dimensional mesh according to local geometries of vertices in the three-dimensional mesh. For example, the disclosed system can determine a local geometry (e.g., a one-ring neighborhood of half-flaps) for each vertex in a three-dimensional mesh. For each subdivision iteration, the disclosed system can then utilize a neural network to determine displacement coordinates for existing vertices in the three-dimensional mesh and coordinates for new vertices added to edges between the existing vertices in the three-dimensional mesh in accordance with the local geometries of the existing vertices. Furthermore, the disclosed system can generate a subdivided three-dimensional mesh based on the determined displacement coordinates for the existing vertices and the determined coordinates for the new vertices.

Abstract: Certain embodiments involve techniques for generating a 3D representation based on a provided 2D image of an object. An image generation system receives the 2D image representation and generates a multi-dimensional vector of the input that represents the image. The image generation system samples a set of points and provides the set of points and the multi-dimensional vector to a neural network that was trained to predict a 3D surface representing the image such that the 3D surface is consistent with a 3D surface of the object calculated using an implicit function for representing the image. The neural network predicts, based on the multi-dimensional vector and the set of points, the 3D surface representing the object.

Abstract: Embodiments provide systems, methods, and computer storage media for generating a 3D model from a target 2D image or 3D point cloud (e.g., generated by a 3D scan). Given a particular target, a retrieval network retrieves or identifies a source model from a database, and a deformation network deforms the source model to fit the target. In some cases, joint learning is employed to enable the retrieval and deformation networks to jointly learn a deformation-aware retrieval embedding space and an individualized deformation space for each source model. In some cases, the retrieval network retrieves based on distance in the deformation-aware retrieval embedding space, enabling the retrieval module to retrieve a source model that best fits to the target after deformation. In some cases, a deformation is decomposed into a plurality of per-part deformations, and/or and the retrieval embedding space is used to select training data.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for iteratively decimating a three-dimensional mesh utilizing successive self-parameterization. For example, the disclosed system can self-parameterize local geometries of a three-dimensional mesh using surface mappings within a two-dimensional surface mapping space. The disclosed system can collapse edges in the three-dimensional mesh to create new vertices from the collapsed edges. The disclosed system can parameterize the collapsed edges based on the surface mappings to collapse corresponding edges within the surface mapping space. The disclosed system can thus generate a decimated three-dimensional mesh by collapsing edges in the three-dimensional mesh while providing a bijective map between points in the decimated three-dimensional mesh and corresponding points in the three-dimensional mesh.

Abstract: Three-dimensional (3D) mesh segmentation techniques are described. In one example, a geometry segmentation system determines a vertex direction for each vertex in a 3D mesh. A segment generation module is then employed to generate segments (e.g., as developable geometries) from the 3D mesh. To do so, a vertex selection module selects an initial vertex having an associated vertex direction. A face identification module then identifies a face in the 3D mesh using that initial vertex and at least one other vertex. A segment determination module compares the vertex direction associated with the initial vertex with a normal determined for the face. If the vertex direction is orthogonal to the normal (e.g., within a threshold amount), the face is added to the segment, and sets another one of the vertices of the face as the initial vertex and the process repeats.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for iteratively decimating a three-dimensional mesh utilizing successive self-parameterization. For example, the disclosed system can self-parameterize local geometries of a three-dimensional mesh using surface mappings within a two-dimensional surface mapping space. The disclosed system can collapse edges in the three-dimensional mesh to create new vertices from the collapsed edges. The disclosed system can parameterize the collapsed edges based on the surface mappings to collapse corresponding edges within the surface mapping space. The disclosed system can thus generate a decimated three-dimensional mesh by collapsing edges in the three-dimensional mesh while providing a bijective map between points in the decimated three-dimensional mesh and corresponding points in the three-dimensional mesh.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing one or more neural networks to recursively subdivide a three-dimensional mesh according to local geometries of vertices in the three-dimensional mesh. For example, the disclosed system can determine a local geometry (e.g., a one-ring neighborhood of half-flaps) for each vertex in a three-dimensional mesh. For each subdivision iteration, the disclosed system can then utilize a neural network to determine displacement coordinates for existing vertices in the three-dimensional mesh and coordinates for new vertices added to edges between the existing vertices in the three-dimensional mesh in accordance with the local geometries of the existing vertices. Furthermore, the disclosed system can generate a subdivided three-dimensional mesh based on the determined displacement coordinates for the existing vertices and the determined coordinates for the new vertices.

Abstract: Three-dimensional (3D) mesh segmentation techniques are described. In one example, a geometry segmentation system determines a vertex direction for each vertex in a 3D mesh. A segment generation module is then employed to generate segments (e.g., as developable geometries) from the 3D mesh. To do so, a vertex selection module selects an initial vertex having an associated vertex direction. A face identification module then identifies a face in the 3D mesh using that initial vertex and at least one other vertex. A segment determination module compares the vertex direction associated with the initial vertex with a normal determined for the face. If the vertex direction is orthogonal to the normal (e.g., within a threshold amount), the face is added to the segment, and sets another one of the vertices of the face as the initial vertex and the process repeats.