Reproduce “PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust 3D Human Mesh Recovery” on OpenResearch
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About this paper
Wendi Yang, Zihang Jiang, Shang Zhao, S. Kevin Zhou
PostoMETRO introduces a transformer-based method for 3D human mesh recovery that integrates occlusion-robust 2D pose information as discrete "pose tokens" with image features. This approach improves performance on standard benchmarks and demonstrates enhanced robustness in severe occlusion scenarios, achieving state-of-the-art results on several occlusion datasets.
Key insights
- The synergistic fusion of image tokens providing texture and pose tokens offering robust structural relationships significantly enhances 3D human mesh recovery, especially under occlusion.
- Learning a structured, abstract representation of human pose through a VQ-VAE (pose tokens) proves more effective for robust 3D reconstruction than directly using raw 2D coordinate inputs.
- The model's improved robustness extends beyond explicit occlusion scenarios, demonstrating a general enhancement in 3D human mesh recovery performance.
Summary by alphaXiv.
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