LumiForge: Inverse Lighting Curation for Precise Portrait Video Relighting

Precise portrait video relighting requires a moving face to respond faithfully and consistently to a specified environment illumination, far beyond generic lighting stylization. The fundamental obstacle is supervision: OLAT provides accurate directional light responses, but extending such capture to diverse moving faces demands tightly synchronized high-speed cameras and extremely fast illumination control, making large-scale video supervision impractical. We present LumiForge with two simple principles: recover rather than capture, and resolve rather than compress. First, our Inverse Lighting Curation (ILC) recovers source illumination and canonical flat-lit appearance from in-the-wild videos, turning real performances into direct relighting supervision. Applying ILC to 15,191 VFHQ clips yields LumiWild, an explicitly environment-conditioned portrait video corpus with nearly one million frames. Second, our ORBIT conditioning preserves environment illumination as directional light tokens and jointly models subject identity, rather than collapsing lighting into a global appearance code. Together, scalable recovered supervision and resolved illumination enable high-fidelity, temporally consistent, and precisely controllable portrait video relighting, while our environment-map estimator achieves state-of-the-art performance on our held-out portrait-based illumination benchmark.