A primer on motion capture with deep learning: principles, pitfalls, and perspectives
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 …
a hard computational problem. Recent advances in deep learning have tremendously …
Deep learning for event-based vision: A comprehensive survey and benchmarks
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 …
asynchronously and produce event streams encoding the time, pixel position, and polarity …
Physical inertial poser (pip): Physics-aware real-time human motion tracking from sparse inertial sensors
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 …
based approaches since occlusions do not lead to a reduced tracking quality and the …
Humannerf: Efficiently generated human radiance field from sparse inputs
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 …
require using dense multi-view inputs and costly training. They are hence largely limited to …
Intergen: Diffusion-based multi-human motion generation under complex interactions
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 …
human motions. Yet, they largely disregard the multi-human interactions. In this paper, we …
Physcap: Physically plausible monocular 3d motion capture in real time
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 …
progress. However, it is a very challenging and severely ill-posed problem. In consequence …
E2nerf: Event enhanced neural radiance fields from blurry images
Abstract Neural Radiance Fields (NeRF) achieves impressive ren-dering performance by
learning volumetric 3D representation from several images of different views. However, it is …
learning volumetric 3D representation from several images of different views. However, it is …
Neural monocular 3d human motion capture with physical awareness
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 …
capture, which achieves state-of-the-art results in a broad range of challenging scenarios …
Eventnerf: Neural radiance fields from a single colour event camera
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 …
range, vanishingly low motion blur, low latency and low data bandwidth. The field saw …
Evdistill: Asynchronous events to end-task learning via bidirectional reconstruction-guided cross-modal knowledge distillation
Event cameras sense per-pixel intensity changes and produce asynchronous event streams
with high dynamic range and less motion blur, showing advantages over the conventional …
with high dynamic range and less motion blur, showing advantages over the conventional …