44 citations · 108 across the 9 of their papers we have counts for
9 papers
LightIt: Illumination Modeling and Control for Diffusion Models
Peter Kocsis, Julien Philip, Kalyan Sunkavalli +2
We introduce LightIt, a method for explicit illumination control for image generation. Recent generative methods lack lighting control, which is crucial to numerous artistic aspect…
DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model
Yinghao Xu, Hao Tan, Fujun Luan +8
We propose \textbf{DMV3D}, a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion. Our reconstruction model inco…
Controllable Dynamic Appearance for Neural 3D Portraits
ShahRukh Athar, Zhixin Shu, Zexiang Xu +4
Recent advances in Neural Radiance Fields (NeRFs) have made it possible to reconstruct and reanimate dynamic portrait scenes with control over head-pose, facial expressions and vie…
PhotoMat: A Material Generator Learned from Single Flash Photos
Xilong Zhou, Miloš Hašan, Valentin Deschaintre +4
Authoring high-quality digital materials is key to realism in 3D rendering. Previous generative models for materials have been trained exclusively on synthetic data; such data is l…
PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields
Zhengfei Kuang, Fujun Luan, Sai Bi +3
Recent advances in neural radiance fields have enabled the high-fidelity 3D reconstruction of complex scenes for novel view synthesis. However, it remains underexplored how the app…
MatFormer: A Generative Model for Procedural Materials
Paul Guerrero, Miloš Hašan, Kalyan Sunkavalli +3
Procedural material graphs are a compact, parameteric, and resolution-independent representation that are a popular choice for material authoring. However, designing procedural mat…