65 citations · 220 across the 34 of their papers we have counts for
35 papers
Training in Deployment: Human-in-the-loop Online Post-Training for Robot Manipulation
Chun Gu, Xiaofei Wei, Yiming Chen +4
Large pretrained robot foundation models provide strong priors for manipulation, yet adapting them to target tasks with offline teleoperated demonstrations often fails to cover the…
Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation
Jingyu Li, Zhe Liu, Dongnan Hu +10
World action models~(WAMs) have shown great promise for autonomous driving and urban navigation. Built upon Vision-Language-Action models or video generation models, existing appro…
From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation
Yajie Li, Bozhou Zhang, Chun Gu +5
Video generation models offer a promising imagination mechanism for robot manipulation by predicting long-horizon future observations, but effectively exploiting these imagined fut…
See Tomorrow, Act Today: Foresight-Driven Autonomous Driving
Bozhou Zhang, Nan Song, Yuang Wang +3
Current end-to-end autonomous driving planners are fundamentally reactive: they condition on historical and present observations to predict future actions. We argue that autonomous…
Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving
Qiqi Liu, Huan Xu, Jingyu Li +5
Autonomous driving requires reasoning about how the environment evolves and planning actions accordingly. Existing world-model-based approaches typically predict future scenes firs…
UniMotion: A Unified Motion Framework for Simulation, Prediction and Planning
Nan Song, Junzhe Jiang, Jingyu Li +2
Motion simulation, prediction and planning are foundational tasks in autonomous driving, each essential for modeling and reasoning about dynamic traffic scenarios. While often addr…
SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving
Jingyu Li, Junjie Wu, Dongnan Hu +6
Recent end-to-end autonomous driving approaches have leveraged Vision-Language Models (VLMs) to enhance planning capabilities in complex driving scenarios. However, VLMs are inhere…
Future-Aware End-to-End Driving: Bidirectional Modeling of Trajectory Planning and Scene Evolution
Bozhou Zhang, Nan Song, Jingyu Li +3
End-to-end autonomous driving methods aim to directly map raw sensor inputs to future driving actions such as planned trajectories, bypassing traditional modular pipelines. While t…
Dynamic Novel View Synthesis in High Dynamic Range
Kaixuan Zhang, Zhipeng Xiong, Minxian Li +3
High Dynamic Range Novel View Synthesis (HDR NVS) seeks to learn an HDR 3D model from Low Dynamic Range (LDR) training images captured under conventional imaging conditions. Curren…
LMAD: Integrated End-to-End Vision-Language Model for Explainable Autonomous Driving
Nan Song, Bozhou Zhang, Xiatian Zhu +2
Large vision-language models (VLMs) have shown promising capabilities in scene understanding, enhancing the explainability of driving behaviors and interactivity with users. Existi…
DeMo++: Motion Decoupling for Autonomous Driving
Bozhou Zhang, Nan Song, Xiatian Zhu +1
Motion forecasting and planning are tasked with estimating the trajectories of traffic agents and the ego vehicle, respectively, to ensure the safety and efficiency of autonomous d…
ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving
Jingyu Li, Bozhou Zhang, Xin Jin +3
Autonomous driving requires rich contextual comprehension and precise predictive reasoning to navigate dynamic and complex environments safely. Vision-Language Models (VLMs) and Dr…
RealEngine: Simulating Autonomous Driving in Realistic Context
Junzhe Jiang, Nan Song, Jingyu Li +2
Driving simulation plays a crucial role in developing reliable driving agents by providing controlled, evaluative environments. To enable meaningful assessments, a high-quality dri…
TensoFlow: Tensorial Flow-based Sampler for Inverse Rendering
Chun Gu, Xiaofei Wei, Li Zhang +1
Inverse rendering aims to recover scene geometry, material properties, and lighting from multi-view images. Given the complexity of light-surface interactions, importance sampling…
4D Gaussian Splatting: Modeling Dynamic Scenes with Native 4D Primitives
Zeyu Yang, Zijie Pan, Xiatian Zhu +4
Dynamic 3D scene representation and novel view synthesis are crucial for enabling immersive experiences required by AR/VR and metaverse applications. It is a challenging task due t…
Reflective Gaussian Splatting
Yuxuan Yao, Zixuan Zeng, Chun Gu +2
Novel view synthesis has experienced significant advancements owing to increasingly capable NeRF- and 3DGS-based methods. However, reflective object reconstruction remains challeng…
Driving View Synthesis on Free-form Trajectories with Generative Prior
Zeyu Yang, Zijie Pan, Yuankun Yang +2
Driving view synthesis along free-form trajectories is essential for realistic driving simulations, enabling closed-loop evaluation of end-to-end driving policies. Existing methods…
Motion Forecasting in Continuous Driving
Nan Song, Bozhou Zhang, Xiatian Zhu +1
Motion forecasting for agents in autonomous driving is highly challenging due to the numerous possibilities for each agent's next action and their complex interactions in space and…
DeepInteraction++: Multi-Modality Interaction for Autonomous Driving
Zeyu Yang, Nan Song, Wei Li +3
Existing top-performance autonomous driving systems typically rely on the multi-modal fusion strategy for reliable scene understanding. This design is however fundamentally restric…
Tetrahedron Splatting for 3D Generation
Chun Gu, Zeyu Yang, Zijie Pan +2
3D representation is essential to the significant advance of 3D generation with 2D diffusion priors. As a flexible representation, NeRF has been first adopted for 3D representation…
Brain3D: Generating 3D Objects from fMRI
Yuankun Yang, Li Zhang, Ziyang Xie +4
Understanding the hidden mechanisms behind human's visual perception is a fundamental question in neuroscience. To that end, investigating into the neural responses of human mind a…
Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video
Zijie Pan, Zeyu Yang, Xiatian Zhu +1
Generating dynamic 3D object from a single-view video is challenging due to the lack of 4D labeled data. An intuitive approach is to extend previous image-to-3D pipelines by transf…
Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Rendering
Yurui Chen, Chun Gu, Junzhe Jiang +2
Modeling dynamic, large-scale urban scenes is challenging due to their highly intricate geometric structures and unconstrained dynamics in both space and time. Prior methods often…
Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping
Zijie Pan, Jiachen Lu, Xiatian Zhu +1
High-resolution 3D object generation remains a challenging task primarily due to the limited availability of comprehensive annotated training data. Recent advancements have aimed t…