RAM: Recover Any 3D Human Motion in-the-Wild

arXiv cs.CV / 3/23/2026

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Key Points

  • RAM introduces a motion-aware semantic tracker that uses adaptive Kalman filtering to improve identity association under severe occlusions and dynamic interactions.
  • It adds a memory-augmented Temporal HMR module that injects spatio-temporal priors for more consistent and smooth 3D motion estimation.
  • A lightweight Predictor module forecasts future poses to maintain reconstruction continuity, complementing the tracker for robust performance.
  • The approach achieves state-of-the-art results on PoseTrack and 3DPW, demonstrating improved zero-shot tracking stability and 3D accuracy and offering a generalizable, markerless 3D human motion capture paradigm in-the-wild.

Abstract

RAM incorporates a motion-aware semantic tracker with adaptive Kalman filtering to achieve robust identity association under severe occlusions and dynamic interactions. A memory-augmented Temporal HMR module further enhances human motion reconstruction by injecting spatio-temporal priors for consistent and smooth motion estimation. Moreover, a lightweight Predictor module forecasts future poses to maintain reconstruction continuity, while a gated combiner adaptively fuses reconstructed and predicted features to ensure coherence and robustness. Experiments on in-the-wild multi-person benchmarks such as PoseTrack and 3DPW, demonstrate that RAM substantially outperforms previous state-of-the-art in both Zero-shot tracking stability and 3D accuracy, offering a generalizable paradigm for markerless 3D human motion capture in-the-wild.