Film Style Transfer

In film post-production, a colorist needs to apply the visual look of a whole film — its film look — to target frames. The standard approach is manual colour grading, accurate but labour-intensive. We propose to automate this step by introducing a novel computer vision task, termed film style transfer (FST): a set of reference frames sampled from a film represents its film look, which needs to be transferred to target frames. One intuitive solution is to leverage off-the-shelf style transfer methods. However, this suffers several issues: (1) Existing methods often assume a single style image, whereas FST is defined over a set of reference frames; (2) applying the film look uniformly over target frames tends to degrade their fine structure, rendering such direct application suboptimal. To address these, we introduce a dedicated FST method, AnswerPrint, that starts with film look extraction, followed by a coarse-to-fine transformation: first transferring the global film look, then recovering the fine content details the global transfer destroys, echoing the spirit of the established colorist workflow. Our method integrates with existing image generative models (e.g., Stable Diffusion) without extra training. To enable evaluation, we build Reel-30, a new FST benchmark with thirty films as well as metrics in film identity (source-film recognisability) and structural fidelity (local structure preservation). Extensive experiments show that AnswerPrint excels over existing alternatives (e.g., LUT, StyleID). Code and data will be released.