Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation

• URL: http://arxiv.org/abs/2004.02186v2
• Date: Sat, 20 Jun 2020 08:35:57 GMT
• Title: Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation
• Authors: Edoardo Remelli, Shangchen Han, Sina Honari, Pascal Fua, Robert Wang
• Abstract summary: We present a solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. We exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points. Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections.
• Score: 57.11299763566534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a lightweight solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. Building upon recent advances in interpretable representation learning, we exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points. This allows us to reason effectively about 3D pose across different views without using compute-intensive volumetric grids. Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections, that can be simply lifted to 3D via a differentiable Direct Linear Transform (DLT) layer. In order to do it efficiently, we propose a novel implementation of DLT that is orders of magnitude faster on GPU architectures than standard SVD-based triangulation methods. We evaluate our approach on two large-scale human pose datasets (H36M and Total Capture): our method outperforms or performs comparably to the state-of-the-art volumetric methods, while, unlike them, yielding real-time performance.

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