Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that tune a 3D-object-reconstruction-machine-learning model to reconstruct 3D models of objects from real images using real images as training data. For instance, the disclosed systems can determine a depth map for a real two-dimensional (2D) image and then reconstruct a 3D model of a digital object in the real 2D image based on the depth map. By using a depth map for a real 2D image, the disclosed systems can generate reconstructed 3D models that better conform to the shape of digital objects in real images than existing systems and use such reconstructed 3D models to generate more realistic looking visual effects (e.g., shadows, relighting).

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: Certain aspects and features of this disclosure relate to modeling shapes using differentiable, signed distance functions. 3D modeling software can edit a 3D model represented using the differentiable, signed distance functions while displaying the model in a manner that is computing resource efficient and fast. Further, such 3D modeling software can automatically create such an editable 3D model from a reference representation that can be obtained in various ways and stored in a variety of formats. For example, a real-world object can be scanned using LiDAR and a reference representation can be produced from the LiDAR data. Candidate procedural models from a library of curated procedural models are optimized to obtain the best procedural model for editing. A selected procedural model provides an editable, reconstructed shape based on the reference representation of the object.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that tune a 3D-object-reconstruction-machine-learning model to reconstruct 3D models of objects from real images using real images as training data. For instance, the disclosed systems can determine a depth map for a real two-dimensional (2D) image and then reconstruct a 3D model of a digital object in the real 2D image based on the depth map. By using a depth map for a real 2D image, the disclosed systems can generate reconstructed 3D models that better conform to the shape of digital objects in real images than existing systems and use such reconstructed 3D models to generate more realistic looking visual effects (e.g., shadows, relighting).

Abstract: A training system includes: a neural network model configured to determine three-dimensional coordinates of joints, respectively, representing poses of animals in images, where the neural network model is trained using a first training dataset including: images including animals; and coordinates of joints of the animals in the images, respectively; and a training module configured to, after the training of the neural network model using the first training dataset, train the neural network model using a second training dataset including motion capture data, where the motion capture data does not include images of animals and includes measured coordinates at points, respectively, on animals.