Yesnt: Are Diffusion Relighting Models Ready for Capture Stage Compositing? A Hybrid Alternative to Bridge the Gap

TL;DR

This paper introduces a hybrid volumetric video relighting method that combines diffusion-based priors with physical rendering and temporal regularization, achieving more stable and scalable results for virtual performances.

Contribution

It presents a novel hybrid framework that integrates diffusion-derived material priors with temporal and physical constraints for improved volumetric relighting.

Findings

Significantly improves temporal stability over diffusion-only methods.

Scales beyond the length limits of current video diffusion models.

Produces more consistent relighting results on real and synthetic data.

Abstract

Volumetric video relighting is essential for bringing captured performances into virtual worlds, but current approaches struggle to deliver temporally stable, production-ready results. Diffusion-based intrinsic decomposition methods show promise for single frames, yet suffer from stochastic noise and instability when extended to sequences, while video diffusion models remain constrained by memory and scale. We propose a hybrid relighting framework that combines diffusion-derived material priors with temporal regularization and physically motivated rendering. Our method aggregates multiple stochastic estimates of per-frame material properties into temporally consistent shading components, using optical-flow-guided regularization. For indirect effects such as shadows and reflections, we extract a mesh proxy from Gaussian Opacity Fields and render it within a standard graphics pipeline. Experiments on real and synthetic captures show that this hybrid strategy achieves substantially more stable relighting across sequences than diffusion-only baselines, while scaling beyond the clip lengths feasible for video diffusion. These results indicate that hybrid approaches, which balance learned priors with physically grounded constraints, are a practical step toward production-ready volumetric video relighting.