Estimating human pose and shape from monocular images is a long-standing problem in
computer vision. Since the release of statistical body models, 3D human mesh recovery has …

The computer graphics and vision communities have dedicated long standing efforts in
building computerized tools for reconstructing, tracking, and analyzing human faces based …

Creating high-fidelity 3D head avatars has always been a research hotspot but there
remains a great challenge under lightweight sparse view setups. In this paper we propose …

In this paper, we propose HeadNeRF, a novel NeRF-based parametric head model that
integrates the neural radiance field to the parametric representation of the human head. It …

Traditional 3D morphable face models (3DMMs) provide fine-grained control over
expression but cannot easily capture geometric and appearance details. Neural volumetric …

As 3D facial avatars become more widely used for communication, it is critical that they
faithfully convey emotion. Unfortunately, the best recent methods that regress parametric 3D …

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 present a novel semantic model for human head defined with neural radiance field. The
3D-consistent head model consist of a set of disentangled and interpretable bases, and can …

Most existing monocular 3D pose estimation approaches only focus on a single body part,
neglecting the fact that the essential nuance of human motion is conveyed through a concert …

In this work we propose Identity Consistency Transformer, a novel face forgery detection
method that focuses on high-level semantics, specifically identity information, and detecting …