Researchers have achieved great success in dealing with 2D images using deep learning.
In recent years, 3D computer vision and geometry deep learning have gained ever more …

Novel view synthesis for dynamic scenes is still a challenging problem in computer vision
and graphics. Recently Gaussian splatting has emerged as a robust technique to represent …

Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great
potential in novel view synthesis of a scene. However, current NeRF-based methods cannot …

Virtual reality (VR) offers an artificial, computer generated simulation of a real life
environment. It originated in the 1960s and has evolved to provide increasing immersion …

Polygonal meshes are ubiquitous in the digital 3D domain, yet they have only played a
minor role in the deep learning revolution. Leading methods for learning generative models …

While implicit representations have achieved high-fidelity results in 3D rendering, it remains
challenging to deforming and animating the implicit field. Existing works typically leverage …

We introduce SDM-NET, a deep generative neural network which produces structured
deformable meshes. Specifically, the network is trained to generate a spatial arrangement of …

We are seeing a Cambrian explosion of 3D shape representations for use in machine
learning. Some representations seek high expressive power in capturing high-resolution …

We present the first method that automatically transfers poses between stylized 3D
characters without skeletal rigging. In contrast to previous attempts to learn pose …

In this paper, we present a novel strategy to design disentangled 3D face shape
representation. Specifically, a given 3D face shape is decomposed into identity part and …