Multi-Style Facial Sketch Synthesis through Masked Generative Modeling

• URL: http://arxiv.org/abs/2408.12400v1
• Date: Thu, 22 Aug 2024 13:45:04 GMT
• Title: Multi-Style Facial Sketch Synthesis through Masked Generative Modeling
• Authors: Bowen Sun, Guo Lu, Shibao Zheng,
• Abstract summary: We propose a lightweight end-to-end synthesis model that efficiently converts images to corresponding multi-stylized sketches. In this study, we overcome the issue of data insufficiency by incorporating semi-supervised learning into the training process. Our method consistently outperforms previous algorithms across multiple benchmarks.
• Score: 17.313050611750413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The facial sketch synthesis (FSS) model, capable of generating sketch portraits from given facial photographs, holds profound implications across multiple domains, encompassing cross-modal face recognition, entertainment, art, media, among others. However, the production of high-quality sketches remains a formidable task, primarily due to the challenges and flaws associated with three key factors: (1) the scarcity of artist-drawn data, (2) the constraints imposed by limited style types, and (3) the deficiencies of processing input information in existing models. To address these difficulties, we propose a lightweight end-to-end synthesis model that efficiently converts images to corresponding multi-stylized sketches, obviating the necessity for any supplementary inputs (\eg, 3D geometry). In this study, we overcome the issue of data insufficiency by incorporating semi-supervised learning into the training process. Additionally, we employ a feature extraction module and style embeddings to proficiently steer the generative transformer during the iterative prediction of masked image tokens, thus achieving a continuous stylized output that retains facial features accurately in sketches. The extensive experiments demonstrate that our method consistently outperforms previous algorithms across multiple benchmarks, exhibiting a discernible disparity.

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