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 …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only
collections of single-view 2D photographs has very recently made much progress. These 3D …

We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and
lighting of an articulated animal like a horse given a single test image as input. We present a …

Creation of 3D content by stylization is a promising yet challenging problem in computer
vision and graphics research. In this work, we focus on stylizing photorealistic appearance …

Recent advances in radiance fields enable photorealistic rendering of static or dynamic 3D
scenes, but still do not support explicit deformation that is used for scene manipulation or …

In this paper, we focus on category-level 6D pose and size estimation from a monocular
RGB-D image. Previous methods suffer from inefficient category-level pose feature …

We present a technique for automatically producing a deformation of an input triangle mesh,
guided solely by a text prompt. Our framework is capable of deformations that produce both …

We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of
a category and generating dense correspondences among shapes. With DIF, a 3D shape is …