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 …

Humans intuitively understand that inanimate objects do not move by themselves, but that
state changes are typically caused by human manipulation (eg, the opening of a book). This …

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 …

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

Understanding how humans use physical contact to interact with the world is key to enabling
human-centric artificial intelligence. While inferring 3D contact is crucial for modeling …

Humans constantly contact objects to move and perform tasks. Thus, detecting human-
object contact is important for building human-centered artificial intelligence. However, there …

We address the problem of inferring the 3D shape and pose of dogs from images. Given the
lack of 3D training data, this problem is challenging, and the best methods lag behind those …

Great progress has been made in estimating 3D human pose and shape from images and
video by training neural networks to directly regress the parameters of parametric human …

Traditional approaches in physics-based motion generation centered around imitation
learning and reward shaping often struggle to adapt to new scenarios. To tackle this …