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20232026
most citedNeural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues

1 citations · 1 across the 17 of their papers we have counts for

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cs.CV2026

NeoWorld-Pro: Programming Interactive Scenes from Monocular Images for Embodied Simulation

Yumeng He, Yichen Song, Xiaotian Yang +5

The advancement of Embodied AI necessitates high-quality simulation assets that faithfully mirror the real world. However, transforming raw visual observations into simulation-read…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024

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…