The combination of deep learning, artist-curated scans, and Implicit Functions (IF), is
enabling the creation of detailed, clothed, 3D humans from images. However, existing …

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 …

We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a
clothed human and turns them into an animatable avatar. These avatars are driven by pose …

Coordinate-based volumetric representations have the potential to generate photo-realistic
virtual avatars from images. However, virtual avatars need to be controllable and be …

Neural implicit surface representations have emerged as a promising paradigm to capture
3D shapes in a continuous and resolution-independent manner. However, adapting them to …

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 …

Most existing geometry processing algorithms use meshes as the default shape
representation. Manipulating meshes, however, requires one to maintain high quality in the …

Building animatable 3D models is challenging due to the need for 3D scans, laborious
registration, and manual rigging. Recently, differentiable rendering provides a pathway to …

Recovering a skeletal shape from a monocular video is a longstanding challenge. Prevailing
nonrigid animal reconstruction methods often adopt a control-point driven animation model …

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