Multi-Style Facial Sketch Synthesis through Masked Generative Modeling
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909293573308416 |
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| author | Sun, Bowen Lu, Guo Zheng, Shibao |
| author_facet | Sun, Bowen Lu, Guo Zheng, Shibao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12400 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Style Facial Sketch Synthesis through Masked Generative Modeling Sun, Bowen Lu, Guo Zheng, Shibao Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Multi-Style Facial Sketch Synthesis through Masked Generative Modeling |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2408.12400 |