STMR：用于手部网格重建的 Spiral Transformer
STMR: Spiral Transformer for Hand Mesh Reconstruction
AI总结：
本文提出 STMR，将螺旋邻居采样融入 Transformer，并通过 MSPFE 与 PPVL 提升特征表达，在 FreiHAND 上实现手部网格重建的最优精度与推理速度。
摘要：
基于 Transformer 的方法和螺旋邻居采样技术的最新进展极大地提升了手部网格重建水平。Transformer 擅长捕捉复杂的顶点关系，而螺旋邻居采样对于利用拓扑结构至关重要。本文巧妙地将螺旋采样集成到 Transformer 架构中，增强其利用网格拓扑的能力，从而在手部网格重建中获得更优性能，并带来显著的精度提升。STMR 为提高模型效率采用了单个图像编码器。为增强其信息提取能力，我们设计了多尺度姿态特征提取（MSPFE）模块，该模块有助于提取丰富的姿态特征，最终提升模型性能。此外，所提出的预定义姿态到顶点提升（PPVL）方法改善了顶点特征表示，进一步提升了重建性能。在 FreiHAND 数据集上的大量实验表明，与相似骨干网络方法相比，STMR 具有最先进的性能和无可比拟的推理速度，展示了其效率和有效性。代码可在 https://github.com/SmallXieGithub/STMR 获取。
英文摘要：
Recent advancements in both transformer-based methods and spiral neighbor sampling techniques have greatly enhanced hand mesh reconstruction. Transformers excel in capturing complex vertex relationships, and spiral neighbor sampling is vital for utilizing topological structures. This paper ingeniously integrates spiral sampling into the Transformer architecture, enhancing its ability to leverage mesh topology for superior performance in hand mesh reconstruction, resulting in substantial accuracy boosts. STMR employs a single image encoder for model efficiency. To augment its information extraction capability, we design the multi-scale pose feature extraction (MSPFE) module, which facilitates the extraction of rich pose features, ultimately enhancing the model's performance. Moreover, the proposed predefined pose-to-vertex lifting (PPVL) method improves vertex feature representation, further boosting reconstruction performance. Extensive experiments on the FreiHAND dataset demonstrate the state-of-the-art performance and unparalleled inference speed of STMR compared with similar backbone methods, showcasing its efficiency and effectiveness. The code is available at https://github.com/SmallXieGithub/STMR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
