1 citations · 1 across the 5 of their papers we have counts for
5 papers
SpatialTree: How Spatial Abilities Branch Out in MLLMs
Yuxi Xiao, Longfei Li, Shen Yan +5
Cognitive science suggests that spatial ability develops progressively-from perception to reasoning and interaction. Yet in multimodal LLMs (MLLMs), this hierarchy remains poorly u…
Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots
Minghuan Liu, Zhengbang Zhu, Xiaoshen Han +12
Modern robotic manipulation primarily relies on visual observations in a 2D color space for skill learning but suffers from poor generalization. In contrast, humans, living in a 3D…
SpatialTrackerV2: 3D Point Tracking Made Easy
Yuxi Xiao, Jianyuan Wang, Nan Xue +7
We present SpatialTrackerV2, a feed-forward 3D point tracking method for monocular videos. Going beyond modular pipelines built on off-the-shelf components for 3D tracking, our app…
What Matters in Building Vision-Language-Action Models for Generalist Robots
Xinghang Li, Peiyan Li, Long Qian +10
To utilize Foundation Vision Language Models (VLMs) for robotic tasks and motion planning, the community has proposed different methods for injecting action components into VLMs an…
Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation
Haotong Lin, Sida Peng, Jingxiao Chen +7
Prompts play a critical role in unleashing the power of language and vision foundation models for specific tasks. For the first time, we introduce prompting into depth foundation m…