@article{diffrelight2024,
  title = {DifFRelight: Diffusion-Based Facial Performance Relighting},
  year = {2024},
  journal = {Eyeline Studios (Netflix)},
  url = {https://www.eyelinestudios.com/research/diffrelight.html},
  abstract = {DifFRelight, presented at SIGGRAPH Asia 2024 by Netflix's Eyeline Studios, introduces a diffusion-based system for relighting facial performance captures from any viewpoint without on-set lighting rigs for each required condition. The approach combines a subject-specific diffusion model with dynamic 3D Gaussian splatting to reconstruct the subject, then applies learned relighting. The result reproduces physically accurate lighting phenomena -- eye reflections, subsurface scattering, self-shadowing -- that previously required manual VFX. This represents a paradigm shift in the digital-human capture pipeline that educators teaching facial performance and visual effects need to understand as it enters production practice.},
  keywords = {generative-ai, vfx-production, character-animation, production-practice},
  note = {AI \& Animation Education Knowledge Base}
}