1 citations · 1 across the 6 of their papers we have counts for
8 papers
ExMesh++: From Multi-View Images to Relightable UV-PBR Mesh Assets via Topology-Adaptive Reconstruction and Decomposition
Chuanjin Fan, Lifan Wu, Wenjie Chang +3
Multi-view reconstruction extends beyond surface recovery to editable and relightable mesh assets. Such assets require well-formed topology, valid UV parameterization, and explicit…
RGBX-Next: Towards Realistic Generative Rendering from G-Buffers
Zheng Zeng, Marco Salvi, Lifan Wu +9
Diffusion models have achieved impressive results in image, video, and streaming generation. However, compared to traditional 3D rendering, they still lack precise control over the…
Diffusion-Based Material Regularization for Physics-Based Inverse Rendering
Jingwang Ling, Lifan Wu, Feng Xu +1
Reconstructing physics-based 3D assets -- geometry, materials, and illumination -- from multi-view images is a core problem in computer graphics and vision, and a prerequisite for…
ExMesh: Explicit Mesh Reconstruction with Topology Adaptation
Chuanjin Fan, Lifan Wu, Wenjie Chang +3
Reconstructing surface meshes from multi-view images has remained a core challenge in recent years. Most existing methods, whether implicit or explicit, depend on intermediate repr…
SkeletonGaussian: Editable 4D Generation through Gaussian Skeletonization
Lifan Wu, Ruijie Zhu, Yubo Ai +1
4D generation has made remarkable progress in synthesizing dynamic 3D objects from input text, images, or videos. However, existing methods often represent motion as an implicit de…
A Generalizable Light Transport 3D Embedding for Global Illumination
Bing Xu, Mukund Varma T, Cheng Wang +5
Global illumination (GI) is essential for realistic rendering but remains computationally expensive due to the complexity of simulating indirect light transport. Recent neural meth…
SAS: Segment Any 3D Scene with Integrated 2D Priors
Zhuoyuan Li, Jiahao Lu, Jiacheng Deng +4
The open vocabulary capability of 3D models is increasingly valued, as traditional methods with models trained with fixed categories fail to recognize unseen objects in complex dyn…
Regularly Varying Random Fields
Lifan Wu, Gennady Samorodnitsky
We study the extremes of multivariate regularly varying random fields. The crucial tools in our study are the tail field and the spectral field, notions that extend the tail and sp…