MetricHMSR：从单目图像恢复度量尺度的人体网格与场景
MetricHMSR: Metric Human Mesh and Scene Recovery from Monocular Images
把相机光线（内参+边界框）编码进网络，并用 Human MoE 将图像特征与光线特征路由到不同专家，从而在单一框架里同时回归人体局部姿态与全局度量 3D 位置；对做单目人体重建、人体-场景联合重建、以及需要把 2D 检测框升级为度量 3D 定位的工作可直接复用。
原文依据 MetricHMSR incorporates camera rays to comprehensively encode both the bounding box information and the intrinsic parameters of perspective projection.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出 MetricHMSR（度量人体网格与场景恢复），一种从单目图像恢复度量尺度人体网格与场景的新方法。由于相机模型中的不切实际假设以及度量感知本身固有的困难，现有方法难以通过统一模块同时完成人体姿态与度量 3D 位置估计。为解决这一局限，MetricHMSR 引入相机光线，以全面编码边界框信息与透视投影的内参。随后我们提出 Human Mixture-of-Experts（MoE），该模型将图像特征与光线特征动态路由至任务专属专家，以对不同数据方面进行专门理解，从而形成一个同时感知局部姿态与全局 3D 位置的统一框架。在上述结果基础上，我们进一步改进现有的单目度量深度估计方法以获得更精确的结果，最终实现人体与场景在 3D 空间中的无缝叠加。大量实验结果表明，所提方法在人体网格恢复与场景恢复两方面均达到最优性能。
英文原文摘要 1181 字符
We introduce MetricHMSR (Metric Human Mesh and Scene Recovery), a novel approach for metric human mesh and scene recovery from monocular images. Due to unrealistic assumptions in the camera model and inherent challenges in metric perception, existing approaches struggle to achieve human pose and metric 3D position estimation through a unified module.To address this limitation, MetricHMSR incorporates camera rays to comprehensively encode both the bounding box information and the intrinsic parameters of perspective projection. Then we proposed Human Mixture-of-Experts (MoE), the model dynamically routes image features and ray features to task-specific experts for specialized understanding of different data aspects, enabling a unified framework that simultaneously perceives the local pose and the global 3D position.Based on the results above, we further refine the existing monocular metric depth estimation method to achieve more accurate results, ultimately enabling the seamless overlay of humans and scenes in 3D space.Comprehensive experimental results demonstrate that the proposed method achieves state-of-the-art performance on both human mesh and scene recovery.