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in this work we present a temporally coherent portrait relighting approach from rgb video input our approach jointly models the semantic temporal and lighting consistency to enable realistic video portrait light editing whilst maintaining real-time performance even on portable device [Music] we build up a data set to provide high quality and diverse video portrait re-lighting samples our capture system consists of a light stage setup with 114 led light sources and a stationary 4k ultra high speed camera at 1000 fps our data set includes 600 3288 temporal olat image sets of 18 females and 18 males with 2810 high dynamic range environment lighting maps in our pipeline we adopt the beta distribution drastically diversifying the lighting combination to model the illumination consistency and mutation simultaneously and enhance our networks generalization ability then based on the u-net structure of we adapted gon self-supervision and face parsing to enhance the disentanglement our network