2 citations · 3 across the 34 of their papers we have counts for
9 papers · 1 filter
EgoLCD: Egocentric Video Generation with Long Context Diffusion
Liuzhou Zhang, Jiarui Ye, Yuanlei Wang +6
Generating long, coherent egocentric videos is difficult, as hand-object interactions and procedural tasks require reliable long-term memory. Existing autoregressive models suffer…
MobileVLA-R1: Reinforcing Vision-Language-Action for Mobile Robots
Ting Huang, Dongjian Li, Rui Yang +3
Grounding natural-language instructions into continuous control for quadruped robots remains a fundamental challenge in vision language action. Existing methods struggle to bridge…
EvoVLA: Self-Evolving Vision-Language-Action Model
Zeting Liu, Zida Yang, Zeyu Zhang +1
Long-horizon robotic manipulation remains challenging for Vision-Language-Action (VLA) models despite recent progress in zero-shot generalization and simulation-to-real-world trans…
UniVid: The Open-Source Unified Video Model
Jiabin Luo, Junhui Lin, Zeyu Zhang +4
Unified video modeling that combines generation and understanding capabilities is increasingly important but faces two key challenges: maintaining semantic faithfulness during flow…
StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes
Zhengri Wu, Yiran Wang, Yu Wen +3
Underwater stereo depth estimation provides accurate 3D geometry for robotics tasks such as navigation, inspection, and mapping, offering metric depth from low-cost passive cameras…
Nav-R1: Reasoning and Navigation in Embodied Scenes
Qingxiang Liu, Ting Huang, Zeyu Zhang +1
Embodied navigation requires agents to integrate perception, reasoning, and action for robust interaction in complex 3D environments. Existing approaches often suffer from incohere…
ReMoMask: Retrieval-Augmented Masked Motion Generation
Zhengdao Li, Siheng Wang, Zeyu Zhang +1
Text-to-Motion (T2M) generation aims to synthesize realistic and semantically aligned human motion sequences from natural language descriptions. However, current approaches face du…
3D-R1: Enhancing Reasoning in 3D VLMs for Unified Scene Understanding
Ting Huang, Zeyu Zhang, Hao Tang
Large vision-language models (VLMs) have made significant strides in 2D visual understanding tasks, sparking interest in extending these capabilities to 3D scene understanding. How…
3D CoCa: Contrastive Learners are 3D Captioners
Ting Huang, Zeyu Zhang, Yemin Wang +1
3D captioning, which aims to describe the content of 3D scenes in natural language, remains highly challenging due to the inherent sparsity of point clouds and weak cross-modal ali…