128 citations · 168 across the 12 of their papers we have counts for
15 papers
T-WLA: Bridging Slow Vision and Fast Touch
Kaichen Zhou, Yuzhen Chen, Hannah Yao +14
In contact-rich manipulation, contact conditions can change before a visual policy updates its action plan. Recomputing the plan through the visual policy adds latency, while tacti…
Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models
Hongyu Li, Wanjia Fu, Xiaoyan Cong +11
Predicting object dynamics (i.e., world modeling) is a fundamental challenge for robotic manipulation, and modeling deformable objects presents a particularly difficult case due to…
OPENTOUCH: Bringing Full-Hand Touch to Real-World Interaction
Yuxin Ray Song, Jinzhou Li, Rao Fu +12
The human hand is our primary interface to the physical world, yet egocentric perception rarely knows when, where, or how forcefully it makes contact. Robust wearable tactile senso…
DyTact: Capturing Dynamic Contacts in Hand-Object Manipulation
Xiaoyan Cong, Angela Xing, Chandradeep Pokhariya +2
Reconstructing dynamic hand-object contacts is essential for realistic manipulation in AI character animation, XR, and robotics, yet it remains challenging due to heavy occlusions,…
Art3D: Training-Free 3D Generation from Flat-Colored Illustration
Xiaoyan Cong, Jiayi Shen, Zekun Li +3
Large-scale pre-trained image-to-3D generative models have exhibited remarkable capabilities in diverse shape generations. However, most of them struggle to synthesize plausible 3D…
GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities
Rao Fu, Dingxi Zhang, Alex Jiang +4
Understanding bimanual human hand activities is a critical problem in AI and robotics. We cannot build large models of bimanual activities because existing datasets lack the scale,…
ScratchEval: Are GPT-4o Smarter than My Child? Evaluating Large Multimodal Models with Visual Programming Challenges
Rao Fu, Ziyang Luo, Hongzhan Lin +2
Recent advancements in large multimodal models (LMMs) have showcased impressive code generation capabilities, primarily evaluated through image-to-code benchmarks. However, these b…
Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoning
Rao Fu, Jingyu Liu, Xilun Chen +2
This paper introduces Scene-LLM, a 3D-visual-language model that enhances embodied agents' abilities in interactive 3D indoor environments by integrating the reasoning strengths of…
CharacterMixer: Rig-Aware Interpolation of 3D Characters
Xiao Zhan, Rao Fu, Daniel Ritchie
We present CharacterMixer, a system for blending two rigged 3D characters with different mesh and skeleton topologies while maintaining a rig throughout interpolation. CharacterMix…
AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes
Rao Fu, Zehao Wen, Zichen Liu +1
Inspired by cognitive theories, we introduce AnyHome, a framework that translates any text into well-structured and textured indoor scenes at a house-scale. By prompting Large Lang…
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language
Aditya Sanghi, Rao Fu, Vivian Liu +5
Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We intr…
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model
Rao Fu, Xiao Zhan, Yiwen Chen +2
We present ShapeCrafter, a neural network for recursive text-conditioned 3D shape generation. Existing methods to generate text-conditioned 3D shapes consume an entire text prompt…
NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation
Trevor Houchens, Cheng-You Lu, Shivam Duggal +2
In visual computing, 3D geometry is represented in many different forms including meshes, point clouds, voxel grids, level sets, and depth images. Each representation is suited for…
HRFormer: High-Resolution Transformer for Dense Prediction
Yuhui Yuan, Rao Fu, Lang Huang +4
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that prod…
RISA-Net: Rotation-Invariant Structure-Aware Network for Fine-Grained 3D Shape Retrieval
Rao Fu, Jie Yang, Jiawei Sun +3
Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class, which requires shape descriptors to be…