1 citations · 1 across the 12 of their papers we have counts for
14 papers
One Scene, Two Depths: Probing Geometric Ambiguity in Monocular Foundation Models
Xiaohao Xu, Feng Xue, Xiang Li +5
A faithful 3D world representation should account for layered geometry, where a single camera ray may contain multiple visible and geometrically valid surfaces. Monocular depth est…
Latent Geometry Beyond Search: Amortizing Planning in World Models
Hoang Nguyen, Xiaohao Xu, Xiaonan Huang
Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these spaces remains challenging. This ra…
Probing Collision Grounding in Vision-Language Models for Safe Human-Robot Collaboration
Jun Wang, Xiaohao Xu, Xiaonan Huang
Safe human--robot collaboration requires more than visual description: a monitor must determine whether the robot body is safely separated, already colliding with the scene or a pe…
When Search Becomes Memory: Accelerating Robot Design Discovery with Self-Evolving Skills
Yunfei Wang, Xiaohao Xu, Yang Li +1
Large language models (LLMs) are increasingly used as proposal generators for evolutionary robot design, yet most loops remain memoryless: simulator results shape the next populati…
The 3D Mirage: Probing and Taming 3D Hallucinations
Hoang Nguyen, Xiaohao Xu, Xiaonan Huang
Monocular depth foundation models achieve remarkable generalization by learning large-scale semantic priors, but this creates a critical vulnerability: they hallucinate illusory 3D…
Whole-Body Proprioceptive Morphing: A Modular Soft Gripper for Robust Cross-Scale Grasping
Dong Heon Han, Xiaohao Xu, Yuxi Chen +5
Biological systems, such as the octopus, exhibit masterful cross-scale manipulation by adaptively reconfiguring their entire form, a capability that remains elusive in robotics. Co…
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark
Yunkang Cao, Yuqi Cheng, Xiaohao Xu +6
The practical deployment of Visual Anomaly Detection (VAD) systems is hindered by their sensitivity to real-world imaging variations, particularly the complex interplay between vie…
ViSA-Flow: Accelerating Robot Skill Learning via Large-Scale Video Semantic Action Flow
Changhe Chen, Quantao Yang, Xiaohao Xu +2
One of the central challenges preventing robots from acquiring complex manipulation skills is the prohibitive cost of collecting large-scale robot demonstrations. In contrast, huma…
Towards Ambiguity-Free Spatial Foundation Model: Rethinking and Decoupling Depth Ambiguity
Xiaohao Xu, Feng Xue, Xiang Li +5
Depth ambiguity is a fundamental challenge in spatial scene understanding, especially in transparent scenes where single-depth estimates fail to capture full 3D structure. Existing…
Natural Selection via Foundation Models for Soft Robot Evolution
Changhe Chen, Xiaohao Xu, Xiangdong Wang +1
Designing soft robots is a complex and iterative process that demands cross-disciplinary expertise in materials science, mechanics, and control, often relying on intuition and exte…
Scalable Benchmarking and Robust Learning for Noise-Free Ego-Motion and 3D Reconstruction from Noisy Video
Xiaohao Xu, Tianyi Zhang, Shibo Zhao +8
We aim to redefine robust ego-motion estimation and photorealistic 3D reconstruction by addressing a critical limitation: the reliance on noise-free data in existing models. While…
Hierarchical Multi-Graphs Learning for Robust Group Re-Identification
Ruiqi Liu, Xingyu Liu, Xiaohao Xu +3
Group Re-identification (G-ReID) faces greater complexity than individual Re-identification (ReID) due to challenges like mutual occlusion, dynamic member interactions, and evolvin…
MAC-Ego3D: Multi-Agent Gaussian Consensus for Real-Time Collaborative Ego-Motion and Photorealistic 3D Reconstruction
Xiaohao Xu, Feng Xue, Shibo Zhao +3
Real-time multi-agent collaboration for ego-motion estimation and high-fidelity 3D reconstruction is vital for scalable spatial intelligence. However, traditional methods produce s…
Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity
Huaxin Zhang, Xiaohao Xu, Xiang Wang +6
How can we enable models to comprehend video anomalies occurring over varying temporal scales and contexts? Traditional Video Anomaly Understanding (VAU) methods focus on frame-lev…