Abstract 3D reconstruction of deformable (or non‐rigid) scenes from a set of monocular 2D
image observations is a long‐standing and actively researched area of computer vision and …

We present AvatarReX, a new method for learning NeRF-based full-body avatars from video
data. The learnt avatar not only provides expressive control of the body, hands and the face …

We propose a novel 3D morphable model for complete human heads based on hybrid
neural fields. At the core of our model lies a neural parametric representation that …

Synthesizing high-quality 3D face models from natural language descriptions is very
valuable for many applications, including avatar creation, virtual reality, and telepresence …

We present HandAvatar, a novel representation for hand animation and rendering, which
can generate smoothly compositional geometry and self-occlusion-aware texture …

Face recognition is one of the most important abilities that we use in our daily lives. There
are several reasons for the growing interest in automated face recognition, including rising …

Abstract We present Monocular Neural Parametric Head Models (MonoNPHM) for dynamic
3D head reconstructions from monocular RGB videos. To this end we propose a latent …

Existing methods for capturing datasets of 3D heads in dense semantic correspondence are
slow and commonly address the problem in two separate steps; multi-view stereo (MVS) …

A human 3D avatar is one of the important elements in the metaverse, and the modeling
effect directly affects people's visual experience. However, the human body has a complex …

Generating 3D models lies at the core of computer graphics and has been the focus of
decades of research. With the emergence of advanced neural representations and …