# TAIHRI：面向近距离人机交互的任务感知3D人体关键点定位

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-10 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmnygovrn003vsl13v5c65r19
- 原文链接：https://arxiv.org/abs/2604.08921

## AI 摘要

腾讯发布首个专为近距离人机交互感知的视觉语言模型TAIHRI，突破传统全身重建范式，实现任务相关身体部位的精确度量级3D空间定位。该模型通过将3D关键点量化为有限交互空间，结合2D关键点推理与下一token预测机制，在自我中心相机坐标系下精准定位关键身体部位。实验表明，TAIHRI在任务关键身体部位估计精度上显著优于传统方法，并支持自然语言控制与全局人体网格重建等下游任务，相关代码已开源。

## 正文

Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventional 3D human keypoints estimation methods primarily focus on the whole-body reconstruction quality relative to the root joint. However, in practical human-robot interaction (HRI) scenarios, robots are more concerned with the precise metric-scale spatial localization of task-relevant body parts under the egocentric camera 3D coordinate. We propose TAIHRI, the first Vision-Language Model (VLM) tailored for close-range HRI perception, capable of understanding users' motion commands and directing the robot's attention to the most task-relevant keypoints. By quantizing 3D keypoints into a finite interaction space, TAIHRI precisely localize the 3D spatial coordinates of critical body parts by 2D keypoint reasoning via next token prediction, and seamlessly adapt to downstream tasks such as natural language control or global space human mesh recovery. Experiments on egocentric interaction benchmarks demonstrate that TAIHRI achieves superior estimation accuracy for task-critical body parts. We believe TAIHRI opens new research avenues in the field of embodied human-robot interaction. Code is available at: https://github.com/Tencent/TAIHRI.