Humans in 4D: Reconstructing and Tracking Humans with Transformers
University of California, Berkeley
Abstract
We present an approach to reconstruct humans and track them over time. At the core of our approach, we propose a fully "transformerized" version of a network for human mesh recovery. This network, HMR 2.0, advances the state of the art and shows the capability to analyze unusual poses that have in the past been difficult to reconstruct from single images. To analyze video, we use 3D reconstructions from HMR 2.0 as input to a tracking system that operates in 3D. This enables us to deal with multiple people and maintain identities through occlusion events. Our complete approach, 4DHumans, achieves state-of-the-art results for tracking people from monocular video. Furthermore, we demonstrate the effectiveness of…
Citation impact
- FWCI
- 22.10
- Percentile
- 100%
- References
- 96
Authors
5Topics & keywords
- Computer science
- Artificial intelligence
- Transformer
- Action recognition
- Computer vision
- Monocular
- Code (set theory)
- Tracking (education)