Search Results for author:
Found 16 papers, 6 papers with code
PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations
This marks a significant advancement towards modeling photorealistic digital humans using physically based inverse rendering with physics in the loop.
GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation
We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from sparse-view images in around 0. 1s.
Gaussian Shell Maps for Efficient 3D Human Generation
Instead of rasterizing the shells directly, we sample 3D Gaussians on the shells whose attributes are encoded in the texture features.
Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior
Our experiments show that our method effectively combines the motion features of the source character with the pose features of the target character, and performs robustly with small or noisy pose data sets, ranging from a few artist-created poses to noisy poses estimated directly from images.
State of the Art on Diffusion Models for Visual Computing
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, editing, and reconstruction of images, videos, and 3D scenes.
Efficient 3D Articulated Human Generation with Layered Surface Volumes
These layers are rendered using alpha compositing with fast differentiable rasterization, and they can be interpreted as a volumetric representation that allocates its capacity to a manifold of finite thickness around the template.
Articulated 3D Head Avatar Generation using Text-to-Image Diffusion Models
Recent work on text-guided 3D object generation has shown great promise in addressing these needs.
DehazeNeRF: Multiple Image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields
Neural radiance fields (NeRFs) have demonstrated state-of-the-art performance for 3D computer vision tasks, including novel view synthesis and 3D shape reconstruction.
PointAvatar: Deformable Point-based Head Avatars from Videos
The ability to create realistic, animatable and relightable head avatars from casual video sequences would open up wide ranging applications in communication and entertainment.
Generative Neural Articulated Radiance Fields
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress.
Input-level Inductive Biases for 3D Reconstruction
Much of the recent progress in 3D vision has been driven by the development of specialized architectures that incorporate geometrical inductive biases.
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields
We present implicit displacement fields, a novel representation for detailed 3D geometry.
Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations
Neural implicit functions have emerged as a powerful representation for surfaces in 3D.
Neural Cages for Detail-Preserving 3D Deformations
The goal of our method is to warp a source shape to match the general structure of a target shape, while preserving the surface details of the source.
Differentiable Surface Splatting for Point-based Geometry Processing
We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for point clouds.
Patch-based Progressive 3D Point Set Upsampling
We present a detail-driven deep neural network for point set upsampling.
