26 citations · 30 across the 11 of their papers we have counts for
13 papers
CosmoH2G: A Hand-to-Gripper Transfer Dataset and Baseline Method for Object Manipulation with Complex Spatial Movements
Hongxiang Zhao, Mutian Xu, Zeyu Jin +3
Transferring human hand demonstrations to robotic grippers has recently emerged as a cost-effective solution for robot learning. However, existing methods are largely confined to s…
Kinema4D: Kinematic 4D World Modeling for Spatiotemporal Embodied Simulation
Mutian Xu, Tianbao Zhang, Tianqi Liu +3
Simulating robot-world interactions is a cornerstone of Embodied AI. Recently, a few works have shown promise in leveraging video generations to transcend the rigid visual/physical…
LoFA: Learning to Predict Personalized Priors for Fast Adaptation of Visual Generative Models
Yiming Hao, Mutian Xu, Chongjie Ye +4
Personalizing visual generative models to meet specific user needs has gained increasing attention, yet current methods like Low-Rank Adaptation (LoRA) remain impractical due to th…
VC-Agent: An Interactive Agent for Customized Video Dataset Collection
Yidan Zhang, Mutian Xu, Yiming Hao +6
Facing scaling laws, video data from the internet becomes increasingly important. However, collecting extensive videos that meet specific needs is extremely labor-intensive and tim…
Stable-Sim2Real: Exploring Simulation of Real-Captured 3D Data with Two-Stage Depth Diffusion
Mutian Xu, Chongjie Ye, Haolin Liu +3
3D data simulation aims to bridge the gap between simulated and real-captured 3D data, which is a fundamental problem for real-world 3D visual tasks. Most 3D data simulation method…
TASTE-Rob: Advancing Video Generation of Task-Oriented Hand-Object Interaction for Generalizable Robotic Manipulation
Hongxiang Zhao, Xingchen Liu, Mutian Xu +3
We address key limitations in existing datasets and models for task-oriented hand-object interaction video generation, a critical approach of generating video demonstrations for ro…
RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D
Lingteng Qiu, Guanying Chen, Xiaodong Gu +7
Lifting 2D diffusion for 3D generation is a challenging problem due to the lack of geometric prior and the complex entanglement of materials and lighting in natural images. Existin…
SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Instance Segmentation
Mutian Xu, Xingyilang Yin, Lingteng Qiu +3
We introduce SAMPro3D for zero-shot instance segmentation of 3D scenes. Given the 3D point cloud and multiple posed RGB-D frames of 3D scenes, our approach segments 3D instances by…
Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks
David Junhao Zhang, Mutian Xu, Chuhui Xue +4
Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additiona…
REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos
Lingteng Qiu, Guanying Chen, Jiapeng Zhou +3
Reconstructing dynamic 3D garment surfaces with open boundaries from monocular videos is an important problem as it provides a practical and low-cost solution for clothes digitizat…
MVImgNet: A Large-scale Dataset of Multi-view Images
Xianggang Yu, Mutian Xu, Yidan Zhang +10
Being data-driven is one of the most iconic properties of deep learning algorithms. The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer…
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective
Chaoqi Chen, Yushuang Wu, Qiyuan Dai +5
Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e.g.,} social…
TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes
Mutian Xu, Pei Chen, Haolin Liu +1
Many basic indoor activities such as eating or writing are always conducted upon different tabletops (e.g., coffee tables, writing desks). It is indispensable to understanding tabl…