用Mamba提升多人姿态估计效率，速度更快精度更高。
EMO-X: Efficient Multi-Person Pose and Shape Estimation in One-Stage
- 结合Mamba与骨骼感知局部扫描，实现全局与局部特征协同优化
- 推理时间比最先进方法减少69.8%，精度仍领先多数模型
- 适合需要实时多人姿态估计的场景，如智能监控或人机交互
表达性人体姿态与形状估计（EHPS）旨在从单目图像中联合估计人体姿态、手部动作和面部表情。现有方法多依赖Transformer架构，其自注意力机制具有二次复杂度，在多人场景下计算开销大。最近，Mamba因其高效的全局建模能力成为Transformer的潜在替代方案，但在捕捉精细局部依赖方面仍有限。为此，我们提出EMO-X：一种高效的一阶段多人体EHPS模型。具体地，设计了基于扫描的全局-局部解码器（SGLD），通过融合全局上下文与骨骼感知的局部特征，迭代增强人体标记。EMO-X利用Mamba优越的全局建模能力，并引入局部双向扫描机制实现骨骼感知的精细化局部优化。大量实验表明，EMO-X在效率与精度间取得良好平衡：相比最先进方法，推理时间减少69.8%，同时在多数指标上表现更优。
原文摘要 · Abstract (English)
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.
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