LumiNet: Latent Intrinsics Meets Diffusion Models for Indoor Scene
  Relighting
Authors: name: Xiaoyan Xing, name: Konrad Groh, name: Sezer Karaoglu, name: Theo Gevers, name: Anand Bhattad

We introduce LumiNet, a novel architecture that leverages generative models
and latent intrinsic representations for effective lighting transfer. Given a
source image and a target lighting image, LumiNet synthesizes a relit version
of the source scene that captures the target's lighting. Our approach makes two
key contributions: a data curation strategy from the StyleGAN-based relighting
model for our training, and a modified diffusion-based ControlNet that
processes both latent intrinsic properties from the source image and latent
extrinsic properties from the target image. We further improve lighting
transfer through a learned adaptor (MLP) that injects the target's latent
extrinsic properties via cross-attention and fine-tuning.
  Unlike traditional ControlNet, which generates images with conditional maps
from a single scene, LumiNet processes latent representations from two
different images - preserving geometry and albedo from the source while
transferring lighting characteristics from the target. Experiments demonstrate
that our method successfully transfers complex lighting phenomena including
specular highlights and indirect illumination across scenes with varying
spatial layouts and materials, outperforming existing approaches on challenging
indoor scenes using only images as input.