Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation

In this paper, we present a novel Distribution-Aware Single-stage (DAS) model for tackling the challenging multi-person 3D pose estimation problem. Different from existing top-down and bottom-up methods, the proposed DAS model simultaneously localizes person positions and their corresponding body joints in the 3D camera space in a one-pass manner. This leads to a simplified pipeline with enhanced efficiency. In addition, DAS learns the true distribution of body joints for the regression of their positions, rather than making a simple Laplacian or Gaussian assumption as previous works. This provides valuable priors for model prediction and thus boosts the regression-based scheme to achieve competitive performance with volumetric-base ones. Moreover, DAS exploits a recursive update strategy for progressively approaching to regression target, alleviating the optimization difficulty and further lifting the regression performance. DAS is implemented with a fully Convolutional Neural Network and end-to-end learnable. Comprehensive experiments on benchmarks CMU Panoptic and MuPoTS-3D demonstrate the superior efficiency of the proposed DAS model, specifically 1.5x speedup over previous best model, and its stat-of-the-art accuracy for multi-person 3D pose estimation.

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
3D Multi-Person Pose Estimation (absolute) MuPoTS-3D DAS 3DPCK 39.2 # 8
3D Multi-Person Pose Estimation (root-relative) MuPoTS-3D DAS 3DPCK 82.7 # 10
3D Multi-Person Pose Estimation Panoptic DAS Average MPJPE (mm) 53.8 # 14


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