Total Relighting:

Learning to Relight Portraits for Background Replacement

Rohit Pandey*, Sergio Orts-Escolano*, Chloe LeGendre*, Christian Haene, Sofien Bouaziz, Christoph Rhemann, Paul Debevec, and Sean Fanello

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We propose a novel system for portrait relighting and background replacement, which maintains high-frequency boundary details and accurately synthesizes the subject’s appearance as lit by novel illumination, thereby producing realistic composite images for any desired scene. Our technique includes foreground estimation via alpha matting, relighting, and compositing. We demonstrate that each of these stages can be tackled in a sequential pipeline without the use of priors (e.g. known background or known illumination) and with no specialized acquisition techniques, using only a single RGB portrait image and a novel, target HDR lighting environment as inputs. We train our model using relit portraits of subjects captured in a light stage computational illumination system, which records multiple lighting conditions, high quality geometry, and accurate alpha mattes. To perform realistic relighting for compositing, we introduce a novel per-pixel lighting representation in a deep learning framework, which explicitly models the diffuse and the specular components of appearance, producing relit portraits with convincingly rendered non-Lambertian effects like specular highlights. Multiple experiments and comparisons show the effectiveness of the proposed approach when applied to in-the-wild images.


		author = {Pandey, Rohit and Orts-Escolano, Sergio, and LeGendre, Chloe and Haene, Christian and
		Bouaziz, Sofien and Rhemann, Christoph and Debevec, Paul and Fanello, Sean},
		title = {Total Relighting: Learning to Relight Portraits for Background Replacement},
		journal = {ACM Transactions on Graphics (Proceedings SIGGRAPH)},
		volume = {40},
		number = {4},
		month = {August},
		year = {2021},
		doi = {10.1145/3450626.3459872},


SIGGRAPH 2021 Technical Video