1 citations · 1 across the 2 of their papers we have counts for
5 papers
SAM 3D: 3Dfy Anything in Images
SAM 3D Team, Xingyu Chen, Fu-Jen Chu +20
We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single image. SAM 3D excels in natural images,…
Multi-SpatialMLLM: Multi-Frame Spatial Understanding with Multi-Modal Large Language Models
Runsen Xu, Weiyao Wang, Hao Tang +5
Multi-modal large language models (MLLMs) have rapidly advanced in visual tasks, yet their spatial understanding remains limited to single images, leaving them ill-suited for physi…
OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB
Yunzhi Lin, Yipu Zhao, Fu-Jen Chu +6
To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset OmniPose6D, crafted to mi…
VideoLifter: Lifting Videos to 3D with Fast Hierarchical Stereo Alignment
Wenyan Cong, Hanqing Zhu, Kevin Wang +6
Efficiently reconstructing 3D scenes from monocular video remains a core challenge in computer vision, vital for applications in virtual reality, robotics, and scene understanding.…
HOIGPT: Learning Long Sequence Hand-Object Interaction with Language Models
Mingzhen Huang, Fu-Jen Chu, Bugra Tekin +10
We introduce HOIGPT, a token-based generative method that unifies 3D hand-object interactions (HOI) perception and generation, offering the first comprehensive solution for caption…