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Hair editing has made tremendous progress in recent years. Early hair editingmethods use well-drawn sketches or masks to specify the editing conditions.Even though they can enable very fine-grained local control, such interactionmodes are inefficient for the editing conditions that can be easily specifiedby language descriptions or reference images. Thanks to the recent breakthroughof cross-modal models (e.g., CLIP), HairCLIP is the first work that enableshair editing based on text descriptions or reference images. However, suchtext-driven and reference-driven interaction modes make HairCLIP unable tosupport fine-grained controls specified by sketch or mask. In this paper, wepropose HairCLIPv2, aiming to support all the aforementioned interactions withone unified framework. Simultaneously, it improves upon HairCLIP with betterirrelevant attributes (e.g., identity, background) preservation and unseen textdescriptions support. The key idea is to convert all the hair editing tasksinto hair transfer tasks, with editing conditions converted into differentproxies accordingly. The editing effects are added upon the input image byblending the corresponding proxy features within the hairstyle or hair colorfeature spaces. Besides the unprecedented user interaction mode support,quantitative and qualitative experiments demonstrate the superiority ofHairCLIPv2 in terms of editing effects, irrelevant attribute preservation andvisual naturalness. Our code is available at\url{https://github.com/wty-ustc/HairCLIPv2}.

HairCLIPv2: Unifying Hair Editing via Proxy Feature Blending