OnlineHMR：基于视频的在线世界坐标人体网格恢复
OnlineHMR: Video-based Online World-Grounded Human Mesh Recovery
OnlineHMR 提供了一种完全在线的人体网格恢复框架，其因果键值缓存和滑动窗口学习策略可复用于其他需要流式推理的时序视觉任务，而增量式SLAM的世界坐标对齐方法为动态场景下的轨迹校正提供了新思路。
原文依据 we propose OnlineHMR, a fully online framework that jointly satisfies four essential criteria of online processing
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论人体网格恢复（HMR）模型从单目视频中重建三维人体，近期工作将其扩展到世界坐标系下的人体轨迹与运动重建。然而，现有方法大多为离线处理，依赖未来帧或全局优化，这限制了其在交互式反馈和感知-动作循环场景（如AR/VR和远程呈现）中的适用性。为此，我们提出OnlineHMR，一个完全在线的框架，同时满足在线处理的四项基本标准：系统级因果性、忠实性、时序一致性和高效性。基于双分支架构，OnlineHMR通过因果键值缓存设计和精心设计的滑动窗口学习策略实现流式推理。同时，以人为中心的增量式SLAM在物理合理的轨迹校正下提供在线世界坐标对齐。实验结果表明，我们的方法在标准EMDB基准和高度动态的自定义视频上取得了与现有基于块的方法相当的性能，同时独特地支持在线处理。论文页面和代码见 https://tsukasane.github.io/Video-OnlineHMR/。
英文原文摘要 1196 字符
Human mesh recovery (HMR) models 3D human body from monocular videos, with recent works extending it to world-coordinate human trajectory and motion reconstruction. However, most existing methods remain offline, relying on future frames or global optimization, which limits their applicability in interactive feedback and perception-action loop scenarios such as AR/VR and telepresence. To address this, we propose OnlineHMR, a fully online framework that jointly satisfies four essential criteria of online processing, including system-level causality, faithfulness, temporal consistency, and efficiency. Built upon a two-branch architecture, OnlineHMR enables streaming inference via a causal key-value cache design and a curated sliding-window learning strategy. Meanwhile, a human-centric incremental SLAM provides online world-grounded alignment under physically plausible trajectory correction. Experimental results show that our method achieves performance comparable to existing chunk-based approaches on the standard EMDB benchmark and highly dynamic custom videos, while uniquely supporting online processing. Page and code are available at https://tsukasane.github.io/Video-OnlineHMR/.