SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation

Recovering multi-person 3D poses with absolute scales from a single RGB image is a challenging problem due to the inherent depth and scale ambiguity from a single view. Addressing this ambiguity requires to aggregate various cues over the entire image, such as body sizes, scene layouts, and inter-person relationships. However, most previous methods adopt a top-down scheme that first performs 2D pose detection and then regresses the 3D pose and scale for each detected person individually, ignoring global contextual cues. In this paper, we propose a novel system that first regresses a set of 2.5D representations of body parts and then reconstructs the 3D absolute poses based on these 2.5D representations with a depth-aware part association algorithm. Such a single-shot bottom-up scheme allows the system to better learn and reason about the inter-person depth relationship, improving both 3D and 2D pose estimation. The experiments demonstrate that the proposed approach achieves the state-of-the-art performance on the CMU Panoptic and MuPoTS-3D datasets and is applicable to in-the-wild videos.

PDF Abstract ECCV 2020 PDF ECCV 2020 Abstract
Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
3D Human Pose Estimation Human3.6M SMAP Average MPJPE (mm) 54.1 # 206
3D Multi-Person Pose Estimation (absolute) MuPoTS-3D SMAP 3DPCK 35.4 # 11
3D Multi-Person Pose Estimation (root-relative) MuPoTS-3D SMAP 3DPCK 73.5 # 16
3D Multi-Person Pose Estimation Panoptic SMAP Average MPJPE (mm) 61.8 # 17


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