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

Current methods for learning realistic and animatable 3D clothed avatars need either posed
3D scans or 2D images with carefully controlled user poses. In contrast, our goal is to learn …

Motion capture from sparse inertial sensors has shown great potential compared to image-
based approaches since occlusions do not lead to a reduced tracking quality and the …

Regression-based methods have recently shown promising results in reconstructing human
meshes from monocular images. By directly mapping raw pixels to model parameters, these …

We present PyMAF-X, a regression-based approach to recovering a parametric full-body
model from a single image. This task is very challenging since minor parametric deviation …

We introduce TADA, a simple-yet-effective approach that takes textual descriptions and
produces expressive 3D avatars with high-quality geometry and lifelike textures, that can be …

Although the estimation of 3D human pose and shape (HPS) is rapidly progressing, current
methods still cannot reliably estimate moving humans in global coordinates, which is critical …

Gaussian splatting has emerged as a powerful 3D representation that harnesses the
advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper we …

We present FaceVerse, a fine-grained 3D Neural Face Model, which is built from hybrid East
Asian face datasets containing 60K fused RGB-D images and 2K high-fidelity 3D head scan …