8 citations · 8 across the 6 of their papers we have counts for
6 papers
TableVerse: A Large-scale Tabletop Dataset with Real-world Grounded Layouts for Generalizable Manipulation
Boyuan Wang, Yue Zhang, Xutao Xue +2
The development of generalizable robotic manipulation policies is inherently bounded by the availability of large-scale, high-fidelity scene data. While recent automated synthesis…
Learning Human-Intention Priors from Large-Scale Human Demonstrations for Robotic Manipulation
Yifan Xie, YuAn Wang, Guangyu Chen +3
Human videos contain rich manipulation priors, but using them for robot learning remains difficult because raw observations entangle scene understanding, human motion, and embodime…
A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model
Kaidong Zhang, Jian Zhang, Rongtao Xu +20
Vision-Language-Action (VLA) models have emerged as a powerful paradigm for open-world robot manipulation, but their practical deployment is often constrained by cost: billion-scal…
Open-Vocabulary Functional 3D Human-Scene Interaction Generation
Jie Liu, Yu Sun, Alpar Cseke +4
Generating 3D humans that functionally interact with 3D scenes remains an open problem with applications in embodied AI, robotics, and interactive content creation. The key challen…
TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representation
Sai Kumar Dwivedi, Yu Sun, Priyanka Patel +2
We address the problem of regressing 3D human pose and shape from a single image, with a focus on 3D accuracy. The current best methods leverage large datasets of 3D pseudo-ground-…
ChatPose: Chatting about 3D Human Pose
Yao Feng, Jing Lin, Sai Kumar Dwivedi +3
We introduce ChatPose, a framework employing Large Language Models (LLMs) to understand and reason about 3D human poses from images or textual descriptions. Our work is motivated b…