TesseTrack: End-to-End Learnable Multi-Person Articulated 3D Pose Tracking
N Dinesh Reddy, Laurent Guigues, Leonid Pishchulin, Jayan Eledath, Srinivasa G. Narasimhan
We consider the task of 3D pose estimation and trackingof multiple people seen in an arbitrary number of camerafeeds. We propose TesseTrack, a novel top-down approachthat simultaneously reasons about multiple individuals' 3Dbody joint reconstructions and associations in space andtime in a single end-to-end learnable framework. At the core of our approach is a novel spatio-temporal formulation that operates in a common voxelized feature space aggregated from single- or multiple-camera views. After a person detection step, a 4D CNN produces short-term person-specific representations which are then linked across time by a differentiable matcher. The linked descriptions are then merged and deconvolved into 3D poses. This joint spatio-temporal formulation contrasts with previous piece-wise strategies that treat 2D pose estimation, 2D-to-3D lifting, and 3D pose tracking as independent sub-problems that are error-prone when solved in isolation. Furthermore, unlike previous methods, TesseTrack is robust to changes in the number of camera views and achieves very good results even if a single view is available at inference time. Quantitative evaluation of 3D pose reconstruction accuracy on standard benchmarks shows significant improvements over the state of the art. Evaluation of multi-person articulated 3D pose tracking in our novel evaluation framework demonstrates the superiority of TesseTrack over strong baselines.
