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Found 10 papers, 7 papers with code
Learning Character-Agnostic Motion for Motion Retargeting in 2D
In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view.
PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes
We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly.
Multimodal Shape Completion via Conditional Generative Adversarial Networks
Several deep learning methods have been proposed for completing partial data from shape acquisition setups, i. e., filling the regions that were missing in the shape.
DeepCAD: A Deep Generative Network for Computer-Aided Design Models
We present the first 3D generative model for a drastically different shape representation --- describing a shape as a sequence of computer-aided design (CAD) operations.
Learning to Generate 3D Shapes from a Single Example
Existing generative models for 3D shapes are typically trained on a large 3D dataset, often of a specific object category.
Implicit Neural Spatial Representations for Time-dependent PDEs
Whereas classical solvers can dynamically adapt their spatial representation only by resorting to complex remeshing algorithms, our INSR approach is intrinsically adaptive.
Zero-1-to-3: Zero-shot One Image to 3D Object
We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image.
Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape
Specifically, we encode the input 3D textured shape into triplane feature maps that represent the signed distance and texture fields of the input.
ReconFusion: 3D Reconstruction with Diffusion Priors
3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes.
