GR3EN: Generative Relighting for 3D Environments (arxiv.org)

A recent innovation in 3D environment relighting, dubbed GR3EN, leverages generative techniques to enhance the realism of large room-scale scene reconstructions. Traditional methods for relighting often face challenges with under-determined or ill-conditioned inverse rendering problems, leading to subpar results in complex scenarios. GR3EN overcomes these hurdles by utilizing outputs from a video-to-video relighting diffusion model, enabling precise control over the lighting conditions in 3D reconstructions without the need to tackle intricate inverse rendering. This advancement is particularly significant for the AI/ML community as it opens the door to improved visualization for applications in architecture, gaming, and virtual reality. By validating the method on both synthetic and real datasets, researchers demonstrate its capacity to render new views of scenes accurately under varied lighting, indicating a robust solution that augments immersive experiences and practical applications in diverse domains. The potential for creating dynamically lit 3D environments marks a crucial step forward in computer vision and highlights the growing intersection of generative models with 3D graphics.