The paper contributes a 12-hour multi-person conversational video dataset with pose and speaking annotations, plus a baseline model for generating full-body talking videos of two to four people.
wav2vec 2.0: A framework for self-supervised learning of speech representations
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Multi-human Interactive Talking Dataset
The paper contributes a 12-hour multi-person conversational video dataset with pose and speaking annotations, plus a baseline model for generating full-body talking videos of two to four people.