Humans in 4D: Reconstructing and Tracking Humans with Transformers
This addresses the challenge of robust human analysis in videos for applications like surveillance or human-computer interaction, but it appears incremental as it builds on existing transformer and mesh recovery methods.
The paper tackles the problem of reconstructing and tracking humans in 3D from monocular video, achieving state-of-the-art results for tracking and improving action recognition performance.
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 HMR 2.0 on the downstream task of action recognition, achieving significant improvements over previous pose-based action recognition approaches. Our code and models are available on the project website: https://shubham-goel.github.io/4dhumans/.