6 papers
PureLight: Learning Complex Luminaires with Light Tracing
Pedro Figueiredo, Zixuan Li, Beibei Wang +2
We propose a neural formulation for estimating the appearance of complex luminaires. We focus on challenging luminaires with complex light transport (e.g., small emitters enclosed…
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…
8DNA: 8D Neural Asset Light Transport by Distribution Learning
Liwen Wu, Haolin Lu, Bing Xu +2
High-fidelity 3D assets exhibit intriguing global illumination effects like subsurface scattering, glossy interreflections, and fine-scale fiber scatterings, which often involve lo…
PureSample: Neural Materials Learned by Sampling Microgeometry
Zixuan Li, Zixiong Wang, Jian Yang +2
Traditional physically-based material models rely on analytically derived bidirectional reflectance distribution functions (BRDFs), typically by considering statistics of micro-pri…
Fiber-level Woven Fabric Capture from a Single Photo
Zixuan Li, Pengfei Shen, Hanxiao Sun +7
Accurately rendering the appearance of fabrics is challenging, due to their complex 3D microstructures and specialized optical properties. If we model the geometry and optics of fa…
Woven Fabric Capture with a Reflection-Transmission Photo Pair
Yingjie Tang, Zixuan Li, Miloš Hašan +2
Digitizing woven fabrics would be valuable for many applications, from digital humans to interior design. Previous work introduces a lightweight woven fabric acquisition approach b…