2 citations · 4 across the 8 of their papers we have counts for
9 papers
MFlowHOI: Whole-Body Human-Object Interaction Generation via Part-Aware MeanFlow
Qiang He, Yonghao Zhang, Yinda Zhang +7
Whole-body human-object interaction (HOI) generation requires coordinating body and finger motion, object trajectories, and time-varying contacts. Existing methods typically rely o…
TCATSeg: A Tooth Center-Wise Attention Network for 3D Dental Model Semantic Segmentation
Qiang He, Wentian Qu, Jiajia Dai +9
Accurate semantic segmentation of 3D dental models is essential for digital dentistry applications such as orthodontics and dental implants. However, due to complex tooth arrangeme…
BFA++: Hierarchical Best-Feature-Aware Token Prune for Multi-View Vision Language Action Model
Haosheng Li, Weixin Mao, Zihan Lan +6
Vision-Language-Action (VLA) models have achieved significant breakthroughs by leveraging Large Vision Language Models (VLMs) to jointly interpret instructions and visual inputs. H…
HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based Data Augmentation
Wentian Qu, Jiahe Li, Jian Cheng +6
Understanding of bimanual hand-object interaction plays an important role in robotics and virtual reality. However, due to significant occlusions between hands and object as well a…
Universal Features Guided Zero-Shot Category-Level Object Pose Estimation
Wentian Qu, Chenyu Meng, Heng Li +6
Object pose estimation, crucial in computer vision and robotics applications, faces challenges with the diversity of unseen categories. We propose a zero-shot method to achieve cat…
Diffgrasp: Whole-Body Grasping Synthesis Guided by Object Motion Using a Diffusion Model
Yonghao Zhang, Qiang He, Yanguang Wan +4
Generating high-quality whole-body human object interaction motion sequences is becoming increasingly important in various fields such as animation, VR/AR, and robotics. The main c…
Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation
Haosheng Li, Weixin Mao, Weipeng Deng +7
Multi-hand semantic grasp generation aims to generate feasible and semantically appropriate grasp poses for different robotic hands based on natural language instructions. Although…
SegGrasp: Zero-Shot Task-Oriented Grasping via Semantic and Geometric Guided Segmentation
Haosheng Li, Weixin Mao, Weipeng Deng +7
Task-oriented grasping, which involves grasping specific parts of objects based on their functions, is crucial for developing advanced robotic systems capable of performing complex…
GRID: Scene-Graph-based Instruction-driven Robotic Task Planning
Zhe Ni, Xiaoxin Deng, Cong Tai +5
Recent works have shown that Large Language Models (LLMs) can facilitate the grounding of instructions for robotic task planning. Despite this progress, most existing works have pr…