5 citations · 6 across the 17 of their papers we have counts for
7 papers · 1 filter
SRUG: Shadow-Guided Relightable Urban Scene with Generation Model
Yonghao Zhao, Zexin Yin, Jian Yang +2
Creating relightable urban scenes from images or videos is widely useful but highly ill-posed. Urban environments are typically unbounded and extend beyond the visible regions. As…
F-RNG: Feed-Forward Relightable Neural Gaussians
Guangming Fu, Jiahui Fan, Jian Yang +2
Capturing relightable 3D assets from real-world objects is a widely researched problem. Several per-scene optimization-based methods, based on 3D Gaussian splatting (3DGS), support…
Relit-LiVE: Relight Video by Jointly Learning Environment Video
Weiqing Xiao, Hong Li, Xiuyu Yang +7
Recent advances have shown that large-scale video diffusion models can be repurposed as neural renderers by first decomposing videos into intrinsic scene representations and then p…
Toward Visually Realistic Simulation: A Benchmark for Evaluating Robot Manipulation in Simulation
Yixin Zhu, Zixiong Wang, Jian Yang +4
Reliable simulation evaluation of robot manipulation policies serves as a high-fidelity proxy for real-world performance. Although existing benchmarks cover a wide range of task ca…
GOR-IS: 3D Gaussian Object Removal in the Intrinsic Space
Yonghao Zhao, Yupeng Gao, Jian Yang +2
Recent advances in Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have made it standard practice to reconstruct 3D scenes from multi-view images. Removing objects f…
FabricGen: Microstructure-Aware Woven Fabric Generation
Yingjie Tang, Di Luo, Zixiong Wang +3
Woven fabric materials are widely used in rendering applications, yet designing realistic examples typically involves multiple stages, requiring expertise in weaving principles and…
Recovering 3D Shapes from Ultra-Fast Motion-Blurred Images
Fei Yu, Shudan Guo, Shiqing Xin +3
We consider the problem of 3D shape recovery from ultra-fast motion-blurred images. While 3D reconstruction from static images has been extensively studied, recovering geometry fro…