Background The study of human movement within sports biomechanics and rehabilitation
settings has made considerable progress over recent decades. However, developing a …

Human volumetric capture is a long-standing topic in computer vision and computer
graphics. Although high-quality results can be achieved using sophisticated off-line systems …

Recent neural human representations can produce high-quality multi-view rendering but
require using dense multi-view inputs and costly training. They are hence largely limited to …

To facilitate the analysis of human actions, interactions and emotions, we compute a 3D
model of human body pose, hand pose, and facial expression from a single monocular …

We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and
lighting of an articulated animal like a horse given a single test image as input. We present a …

Modeling 3D humans accurately and robustly from a single image is very challenging, and
the key for such an ill-posed problem is the 3D representation of the human models. To …

While the accuracy of 3D human pose estimation from images has steadily improved on
benchmark datasets, the best methods still fail in many real-world scenarios. This suggests …

We have recently seen tremendous progress in diffusion advances for generating realistic
human motions. Yet, they largely disregard the multi-human interactions. In this paper, we …

We present a unified deformation model for the markerless capture of multiple scales of
human movement, including facial expressions, body motion, and hand gestures. An initial …

We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D
human reconstruction from a single RGB image. To reduce the ambiguities associated with …