5 papers
DreamPartGen: Semantically Grounded Part-Level 3D Generation via Collaborative Latent Denoising
Tianjiao Yu, Xinzhuo Li, Muntasir Wahed +4
Understanding and generating 3D objects as compositions of meaningful parts is fundamental to human perception and reasoning. However, most text-to-3D methods overlook the semantic…
CausalDrive: Real-time Causal World Models for Autonomous Driving
Tianyi Yan, Huan Zheng, Dubing Chen +10
World models have emerged as a promising paradigm for scaling autonomous driving (AD) data, yet existing video generative models fall short as interactive simulators. Layout-condit…
Phantom: Physics-Infused Video Generation via Joint Modeling of Visual and Latent Physical Dynamics
Ying Shen, Jerry Xiong, Tianjiao Yu +1
Recent advances in generative video modeling, driven by large-scale datasets and powerful architectures, have yielded remarkable visual realism. However, emerging evidence suggests…
PartGS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting
Tianjiao Yu, Vedant Shah, Muntasir Wahed +3
Articulated objects are common in the real world, yet modeling their structure and motion remains a challenging task for 3D reconstruction methods. In this work, we introduce Part$…
RealVLG-R1: A Large-Scale Real-World Visual-Language Grounding Benchmark for Robotic Perception and Manipulation
Linfei Li, Lin Zhang, Ying Shen
Visual-language grounding aims to establish semantic correspondences between natural language and visual entities, enabling models to accurately identify and localize target object…