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 …

This work addresses the problem of generating 3D holistic body motions from human
speech. Given a speech recording, we synthesize sequences of 3D body poses, hand …

Generating realistic 3D worlds occupied by moving humans has many applications in
games, architecture, and synthetic data creation. But generating such scenes is expensive …

Despite recent research advancements in reconstructing clothed humans from a single
image, accurately restoring the “unseen regions” with high-level details remains an …

Recovering whole-body mesh by inferring the abstract pose and shape parameters from
visual content can obtain 3D bodies with realistic structures. However, the inferring process …

The estimation of 3D human motion from video has progressed rapidly but current methods
still have several key limitations. First most methods estimate the human in camera …

Creating high-quality 3D models of clothed humans from single images for real-world
applications is crucial. Despite recent advancements accurately reconstructing humans in …

Despite various probabilistic methods for modeling the uncertainty and ambiguity in human
mesh recovery, their overall precision is limited because existing formulations for joint …

We present C· ASE, an efficient and effective framework that learns Conditional Adversarial
Skill Embeddings for physics-based characters. C· ASE enables the physically simulated …