6 citations · 23 across the 31 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
Improving Masked Autoencoders by Learning Where to Mask
Haijian Chen, Wendong Zhang, Yunbo Wang +1
Masked image modeling is a promising self-supervised learning method for visual data. It is typically built upon image patches with random masks, which largely ignores the variatio…
Continual Predictive Learning from Videos
Geng Chen, Wendong Zhang, Han Lu +4
Predictive learning ideally builds the world model of physical processes in one or more given environments. Typical setups assume that we can collect data from all environments at…
Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid
Wendong Zhang, Yunbo Wang, Bingbing Ni +1
Restoring reasonable and realistic content for arbitrary missing regions in images is an important yet challenging task. Although recent image inpainting models have made significa…
Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation
Shanyan Guan, Jingwei Xu, Michelle Z. He +3
We consider a new problem of adapting a human mesh reconstruction model to out-of-domain streaming videos, where performance of existing SMPL-based models are significantly affecte…
Context-Aware Image Inpainting with Learned Semantic Priors
Wendong Zhang, Junwei Zhu, Ying Tai +5
Recent advances in image inpainting have shown impressive results for generating plausible visual details on rather simple backgrounds. However, for complex scenes, it is still cha…
Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction
Shanyan Guan, Jingwei Xu, Yunbo Wang +2
This paper considers a new problem of adapting a pre-trained model of human mesh reconstruction to out-of-domain streaming videos. However, most previous methods based on the param…
Towards Good Practices of U-Net for Traffic Forecasting
Jingwei Xu, Jianjin Zhang, Zhiyu Yao +1
This technical report presents a solution for the 2020 Traffic4Cast Challenge. We consider the traffic forecasting problem as a future frame prediction task with relatively weak te…