The emergence of 3D Gaussian splatting (3DGS) has greatly accelerated rendering in novel
view synthesis. Unlike neural implicit representations like neural radiance fields (NeRFs) …

3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction,
achieving high quality novel view synthesis and fast rendering speed. However, 3DGS fails …

Modeling animatable human avatars from RGB videos is a long-standing and challenging
problem. Recent works usually adopt MLP-based neural radiance fields (NeRF) to represent …

This work addresses the problem of real-time rendering of photorealistic human body
avatars learned from multi-view videos. While the classical approaches to model and render …

3D Gaussian Splatting (3D-GS) has emerged as a significant advancement in the field of
Computer Graphics, offering explicit scene representation and novel view synthesis without …

The generation of stylistic 3D facial animations driven by speech presents a significant
challenge as it requires learning a many-to-many mapping between speech, style, and the …

Abstract 3D head animation has seen major quality and runtime improvements over the last
few years, particularly empowered by the advances in differentiable rendering and neural …

We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from
sparse-view images in around 0.1 s. GRM is a feed-forward transformer-based model that …

Radiance fields have demonstrated impressive performance in synthesizing lifelike 3D
talking heads. However, due to the difficulty in fitting steep appearance changes, the …

We introduce 3D Gaussian blendshapes for modeling photorealistic head avatars. Taking a
monocular video as input, we learn a base head model of neutral expression, along with a …