Sketch-Guided Anime Hair Editing Using Multimodal Diffusion Transformer (Student Abstract)

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Authors: Association for Artificial Intelligence 2026, I‐Chao Shen, Haoran Xie, Tianyu Zhang
Cited by: 0

Abstract

Anime hair design is crucial but challenging, as it conveys personality and emotion through stylized geometry and layered structure. In this work, we propose a sketch-guided approach for intuitive control of multimodal diffusion transformers (MMDiT) to generate semantically consistent anime hairstyles. We adopt a wisp-level flowline input integrated with a fine-tuned MMDiT to transfer hairstyles while preserving character identity. We believe that this fine-grained sketch control within the MMDiT framework may offer a promising path for structured anime hair editing.

Fields of Study & Subjects

Generative Adversarial Networks and Image Synthesis 3D Shape Modeling and Analysis Computer Graphics and Visualization Techniques

Publication details

Journal / source
Underline Science Inc.
Publication date
7 January 2026
Language
EN