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Although the estimation of 3D human pose and shape (HPS) is rapidlyprogressing, current methods still cannot reliably estimate moving humans inglobal coordinates, which is critical for many applications. This isparticularly challenging when the camera is also moving, entangling human andcamera motion. To address these issues, we adopt a novel 5D representation(space, time, and identity) that enables end-to-end reasoning about people inscenes. Our method, called TRACE, introduces several novel architecturalcomponents. Most importantly, it uses two new "maps" to reason about the 3Dtrajectory of people over time in camera, and world, coordinates. An additionalmemory unit enables persistent tracking of people even during long occlusions.TRACE is the first one-stage method to jointly recover and track 3D humans inglobal coordinates from dynamic cameras. By training it end-to-end, and usingfull image information, TRACE achieves state-of-the-art performance on trackingand HPS benchmarks. The code and dataset are released for research purposes.

TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments