研究人员开发了InterMesh，一个多人物体姿态恢复的新框架，该框架明确纳入了人与环境的交互信息。通过用结构化的交互语义丰富查询表示，这种方法增强了姿态和形状估计，并在基准数据集上取得了显著改进。另外，Anny-Fit被引入为一个全年龄段3D人体网格恢复的多人优化框架，它联合优化个体并利用各种形式的专家知识来提高准确性和连贯性。 AI
影响 人体网格恢复领域的这些进步可以通过实现更准确和更具上下文感知能力的3D人体建模来改进机器人、增强现实和动画等应用。
排序理由 arXiv上提出了两篇关于3D人体网格恢复新方法的独立研究论文。
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Humans constantly interact with their surroundings. Existing end-to-end multi-person human mesh recovery methods, typically based on the DETR framework, capture inter-human relationships through self-attention across all human queries. However, these approaches model interactions…
arXiv:2605.04554v1 Announce Type: new Abstract: Humans constantly interact with their surroundings. Existing end-to-end multi-person human mesh recovery methods, typically based on the DETR framework, capture inter-human relationships through self-attention across all human queri…
arXiv:2605.04728v1 Announce Type: new Abstract: Recovering 3D human pose and shape from a single image remains a cornerstone of human-centric vision, yet most methods assume adult subjects and optimize each person independently. These assumptions fail in real-world, all-age scene…
Recovering 3D human pose and shape from a single image remains a cornerstone of human-centric vision, yet most methods assume adult subjects and optimize each person independently. These assumptions fail in real-world, all-age scenes, where body proportions and depth must be reso…
Humans constantly interact with their surroundings. Existing end-to-end multi-person human mesh recovery methods, typically based on the DETR framework, capture inter-human relationships through self-attention across all human queries. However, these approaches model interactions…