48 citations · 62 across the 29 of their papers we have counts for
30 papers
NeoWorld-Pro: Programming Interactive Scenes from Monocular Images for Embodied Simulation
Yumeng He, Eachen Soong, Yichen Song +6
The advancement of Embodied AI necessitates high-quality simulation assets that faithfully mirror the real world. However, transforming raw visual observations into simulation-read…
LASER: Learning Active Sensing for Continuum Field Reconstruction
Huayu Deng, Jinghui Zhong, Xiangming Zhu +2
High-fidelity measurements of continuum physical fields are essential for scientific discovery and engineering design but remain challenging under sparse and constrained sensing. C…
Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling
Haochen Yuan, Yichen Song, Yunbo Wang +1
Modern time series architectures face a fundamental trade-off: channel-independent models scale well with increasing data volume but ignore critical inter-channel dependencies, whi…
VDFP: Video Deflickering with Flicker-banding Priors
Zhiyi Zhou, Libo Zhu, Zihan Zhou +2
Capturing digital screens with smartphones frequently induces severe banding due to hardware synchronization mismatches. Existing video restoration methods struggle with these stru…
OrbiSim: World Models as Differentiable Physics Engines for Embodied Intelligence
Jiajian Li, Jingyuan Huang, Junru Gong +3
We present OrbiSim, a novel robotic simulation paradigm that redefines world models as a fully differentiable physics engine for embodied intelligence. Unlike prior world models th…
EMGauss: Continuous Slice-to-3D Reconstruction via Dynamic Gaussian Modeling in Volume Electron Microscopy
Yumeng He, Zanwei Zhou, Yekun Zheng +3
Volume electron microscopy (vEM) enables nanoscale 3D imaging of biological structures but remains constrained by acquisition trade-offs, leading to anisotropic volumes with limite…
Goal-Driven Reward by Video Diffusion Models for Reinforcement Learning
Qi Wang, Mian Wu, Yuyang Zhang +7
Reinforcement Learning (RL) has achieved remarkable success in various domains, yet it often relies on carefully designed programmatic reward functions to guide agent behavior. Des…
NeoWorld: Neural Simulation of Explorable Virtual Worlds via Progressive 3D Unfolding
Yanpeng Zhao, Shanyan Guan, Yunbo Wang +3
We introduce NeoWorld, a deep learning framework for generating interactive 3D virtual worlds from a single input image. Inspired by the on-demand worldbuilding concept in the scie…
Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization
Haochen Yuan, Minting Pan, Yunbo Wang +3
Reinforcement learning (RL) has shown significant promise for sequential portfolio optimization tasks, such as stock trading, where the objective is to maximize cumulative returns…
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach
Minting Pan, Yitao Zheng, Jiajian Li +2
Offline reinforcement learning (RL) enables policy optimization using static datasets, avoiding the risks and costs of extensive real-world exploration. However, it struggles with…
Plasticine: Accelerating Research in Plasticity-Motivated Deep Reinforcement Learning
Mingqi Yuan, Qi Wang, Guozheng Ma +8
Developing lifelong learning agents is crucial for artificial general intelligence (AGI). However, deep reinforcement learning (RL) systems often suffer from plasticity loss, where…
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning
Qi Wang, Zhipeng Zhang, Baao Xie +6
Training visual reinforcement learning (RL) in practical scenarios presents a significant challenge, RL agents suffer from low sample efficiency in environments wi…
DimINO: Dimension-Informed Neural Operator Learning
Yichen Song, Yalun Wu, Yunbo Wang +1
In computational physics, a longstanding challenge lies in finding numerical solutions to partial differential equations (PDEs). Recently, research attention has increasingly focus…
EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions
Huayu Deng, Xiangming Zhu, Yunbo Wang +1
Graph neural networks have been a powerful tool for mesh-based physical simulation. To efficiently model large-scale systems, existing methods mainly employ hierarchical graph stru…
Open-World Reinforcement Learning over Long Short-Term Imagination
Jiajian Li, Qi Wang, Yunbo Wang +4
Training visual reinforcement learning agents in a high-dimensional open world presents significant challenges. While various model-based methods have improved sample efficiency by…
ReAugment: Model Zoo-Guided RL for Few-Shot Time Series Augmentation and Forecasting
Haochen Yuan, Yutong Wang, Yihong Chen +2
Time series forecasting, particularly in few-shot learning scenarios, is challenging due to the limited availability of high-quality training data. To address this, we present a pi…
Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering
Yanpeng Zhao, Yiwei Hao, Siyu Gao +2
Learning object-centric representations from unsupervised videos is challenging. Unlike most previous approaches that focus on decomposing 2D images, we present a 3D generative mod…
Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video
Xiangming Zhu, Huayu Deng, Haochen Yuan +2
We introduce latent intuitive physics, a transfer learning framework for physics simulation that can infer hidden properties of fluids from a single 3D video and simulate the obser…
MetaGS: A Meta-Learned Gaussian-Phong Model for Out-of-Distribution 3D Scene Relighting
Yumeng He, Yunbo Wang, Xiaokang Yang
Out-of-distribution (OOD) 3D relighting requires novel view synthesis under unseen lighting conditions that differ significantly from the observed images. Existing relighting metho…
Model-Based Reinforcement Learning with Multi-Task Offline Pretraining
Minting Pan, Yitao Zheng, Yunbo Wang +1
Pretraining reinforcement learning (RL) models on offline datasets is a promising way to improve their training efficiency in online tasks, but challenging due to the inherent mism…
Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues
Chen Yang, Kailing Wang, Yuehao Wang +2
Reconstructing deformable tissues from endoscopic stereo videos in robotic surgery is crucial for various clinical applications. However, existing methods relying only on implicit…
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning
Qi Wang, Junming Yang, Yunbo Wang +3
Training offline RL models using visual inputs poses two significant challenges, i.e., the overfitting problem in representation learning and the overestimation bias for expected f…
DynaVol: Unsupervised Learning for Dynamic Scenes through Object-Centric Voxelization
Yanpeng Zhao, Siyu Gao, Yunbo Wang +1
Unsupervised learning of object-centric representations in dynamic visual scenes is challenging. Unlike most previous approaches that learn to decompose 2D images, we present DynaV…
FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead
Kang Chen, Tao Han, Junchao Gong +11
We present FengWu, an advanced data-driven global medium-range weather forecast system based on Artificial Intelligence (AI). Different from existing data-driven weather forecast m…