384 citations · 447 across the 6 of their papers we have counts for
17 papers
HSPACE: Synthetic Parametric Humans Animated in Complex Environments
Eduard Gabriel Bazavan, Andrei Zanfir, Mihai Zanfir +3
Advances in the state of the art for 3d human sensing are currently limited by the lack of visual datasets with 3d ground truth, including multiple people, in motion, operating in…
THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers
Mihai Zanfir, Andrei Zanfir, Eduard Gabriel Bazavan +3
We present THUNDR, a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people, given monocular RGB images. Key to our methodology is an inte…
Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus
Marius Leordeanu, Mihai Pirvu, Dragos Costea +3
We address the challenging problem of semi-supervised learning in the context of multiple visual interpretations of the world by finding consensus in a graph of neural networks. Ea…
The End-of-End-to-End: A Video Understanding Pentathlon Challenge (2020)
Samuel Albanie, Yang Liu, Arsha Nagrani +18
We present a new video understanding pentathlon challenge, an open competition held in conjunction with the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.…
Speech2Action: Cross-modal Supervision for Action Recognition
Arsha Nagrani, Chen Sun, David Ross +3
Is it possible to guess human action from dialogue alone? In this work we investigate the link between spoken words and actions in movies. We note that movie screenplays describe a…
Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows
Andrei Zanfir, Eduard Gabriel Bazavan, Hongyi Xu +3
Monocular 3D human pose and shape estimation is challenging due to the many degrees of freedom of the human body and thedifficulty to acquire training data for large-scale supervis…