原点を守る：スムーズな遷移を伴う連続画像スタイライゼーション
Staying True to the Origin: Continuous Image Stylization with Smooth Transitions
本論文は、画像の内容を保ちながらスタイルを連続的に制御できるスタイライゼーション手法を提案する。2段階の訓練戦略とスタイル強度を考慮したスプライン補間により、離散的な強度レベルから連続的なスタイル操作を実現する。
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著者: Rui Xu, Hanmo Zhang, Songhua Liu
分類: cs.CV
原文アブストラクト
Recent advances in generative models have achieved remarkable performance in text- and image-conditioned editing. However, preserving the content of a given image while referencing style patterns from another remains challenging, often leading to uncontrollable stylization results. In this paper, we approach image stylization from the perspective of continuous control, aiming to enable modern Diffusion Transformer (DiT)-based multi-reference editing models to (1) faithfully preserve the semantic structure of the content image, (2) render strong stylization effects, and (3) smoothly transition between the two. To this end, we propose a simple yet effective two-stage training strategy along with a style-strength-aware spline formulation. Specifically, in the first stage, the model is trained to produce strongly stylized outputs while preserving the content semantics as much as possible. In the second stage, with the base model frozen, we learn a set of anchor projectors that map various stylization strengths into the model parameter space. During inference, by performing style-strength-aware spline interpolation in a low-rank space, our method enables continuous control over stylization strength, even though the model is trained with only a few discrete strength levels. Extensive experiments demonstrate that our method supports precise and continuous manipulation of stylization strength while generating high-fidelity results with modern DiT models. Project page: https://reychiaro.github.io/StyleController.