Light Up Your Face: A Physically Consistent Dataset and Diffusion Model for Face Fill-Light Enhancement

TL;DR

This paper introduces a large-scale dataset and a diffusion model for face fill-light enhancement that preserves scene illumination and background, enabling controllable, high-fidelity face lighting improvements.

Contribution

The work presents LightYourFace-160K, a physically consistent dataset, and a novel diffusion-based model for face fill-light enhancement with controllable lighting parameters.

Findings

High perceptual quality in face fill-light enhancement

Competitive metrics with better background preservation

Efficient one-step diffusion model for controllable lighting

Abstract

Face fill-light enhancement (FFE) brightens underexposed faces by adding virtual fill light while keeping the original scene illumination and background unchanged. Most face relighting methods aim to reshape overall lighting, which can suppress the input illumination or modify the entire scene, leading to foreground-background inconsistency and mismatching practical FFE needs. To support scalable learning, we introduce LightYourFace-160K (LYF-160K), a large-scale paired dataset built with a physically consistent renderer that injects a disk-shaped area fill light controlled by six disentangled factors, producing 160K before-and-after pairs. We first pretrain a physics-aware lighting prompt (PALP) that embeds the 6D parameters into conditioning tokens, using an auxiliary planar-light reconstruction objective. Building on a pretrained diffusion backbone, we then train a fill-light diffusion (FiLitDiff), an efficient one-step model conditioned on physically grounded lighting codes, enabling controllable and high-fidelity fill lighting at low computational cost. Experiments on held-out paired sets demonstrate strong perceptual quality and competitive full-reference metrics, while better preserving background illumination. The dataset and model will be at https://github.com/gobunu/Light-Up-Your-Face.