Event cameras are bio-inspired sensors that capture the per-pixel intensity changes
asynchronously and produce event streams encoding the time, pixel position, and polarity …

Extracting behavioral measurements non-invasively from video is stymied by the fact that it is
a hard computational problem. Recent advances in deep learning have tremendously …

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 …

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 …

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 …

Abstract Neural Radiance Fields (NeRF) achieves impressive ren-dering performance by
learning volumetric 3D representation from several images of different views. However, it is …

Neuromorphic vision sensors or event cameras have made the visual perception of
extremely low reaction time possible, opening new avenues for high-dynamic robotics …

Asynchronously operating event cameras find many applications due to their high dynamic
range, vanishingly low motion blur, low latency and low data bandwidth. The field saw …

Marker-less 3D human motion capture from a single colour camera has seen significant
progress. However, it is a very challenging and severely ill-posed problem. In consequence …

We present a new trainable system for physically plausible markerless 3D human motion
capture, which achieves state-of-the-art results in a broad range of challenging scenarios …