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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.08718 |
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| _version_ | 1866918220284297216 |
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| author | Jian, Haohang Zhang, Jinlu Wu, Junyi Tu, Zhigang |
| author_facet | Jian, Haohang Zhang, Jinlu Wu, Junyi Tu, Zhigang |
| contents | Expressive Human Pose and Shape Estimation (EHPS) aims to jointly estimate human pose, hand gesture, and facial expression from monocular images. Existing methods predominantly rely on Transformer-based architectures, which suffer from quadratic complexity in self-attention, leading to substantial computational overhead, especially in multi-person scenarios. Recently, Mamba has emerged as a promising alternative to Transformers due to its efficient global modeling capability. However, it remains limited in capturing fine-grained local dependencies, which are essential for precise EHPS. To address these issues, we propose EMO-X, the Efficient Multi-person One-stage model for multi-person EHPS. Specifically, we explore a Scan-based Global-Local Decoder (SGLD) that integrates global context with skeleton-aware local features to iteratively enhance human tokens. Our EMO-X leverages the superior global modeling capability of Mamba and designs a local bidirectional scan mechanism for skeleton-aware local refinement. Comprehensive experiments demonstrate that EMO-X strikes an excellent balance between efficiency and accuracy. Notably, it achieves a significant reduction in computational complexity, requiring 69.8% less inference time compared to state-of-the-art (SOTA) methods, while outperforming most of them in accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08718 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EMO-X: Efficient Multi-Person Pose and Shape Estimation in One-Stage Jian, Haohang Zhang, Jinlu Wu, Junyi Tu, Zhigang Computer Vision and Pattern Recognition Expressive Human Pose and Shape Estimation (EHPS) aims to jointly estimate human pose, hand gesture, and facial expression from monocular images. Existing methods predominantly rely on Transformer-based architectures, which suffer from quadratic complexity in self-attention, leading to substantial computational overhead, especially in multi-person scenarios. Recently, Mamba has emerged as a promising alternative to Transformers due to its efficient global modeling capability. However, it remains limited in capturing fine-grained local dependencies, which are essential for precise EHPS. To address these issues, we propose EMO-X, the Efficient Multi-person One-stage model for multi-person EHPS. Specifically, we explore a Scan-based Global-Local Decoder (SGLD) that integrates global context with skeleton-aware local features to iteratively enhance human tokens. Our EMO-X leverages the superior global modeling capability of Mamba and designs a local bidirectional scan mechanism for skeleton-aware local refinement. Comprehensive experiments demonstrate that EMO-X strikes an excellent balance between efficiency and accuracy. Notably, it achieves a significant reduction in computational complexity, requiring 69.8% less inference time compared to state-of-the-art (SOTA) methods, while outperforming most of them in accuracy. |
| title | EMO-X: Efficient Multi-Person Pose and Shape Estimation in One-Stage |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.08718 |