ShanghaiTech University Knowledge Management System
| 2024-11-25 | |
| 状态 | 已发表 |
| 摘要 | This paper presents VQ-SGen, a novel algorithm for high-quality sketch generation. Recent approaches have often framed the task as pixel-based generation either as a whole or part-by-part, neglecting the intrinsic and contextual relationships among individual strokes, such as the shape and spatial positioning of both proximal and distant strokes. To overcome these limitations, we propose treating each stroke within a sketch as an entity and introducing a vector-quantized (VQ) stroke representation for fine-grained sketch generation. Our method follows a two-stage framework - in the first stage, we decouple each stroke's shape and location information to ensure the VQ representation prioritizes stroke shape learning. In the second stage, we feed the precise and compact representation into an auto-decoding Transformer to incorporate stroke semantics, positions, and shapes into the generation process. By utilizing tokenized stroke representation, our approach generates strokes with high fidelity and facilitates novel applications, such as conditional generation and semantic-aware stroke editing. Comprehensive experiments demonstrate our method surpasses existing state-of-the-art techniques, underscoring its effectiveness. The code and model will be made publicly available upon publication. |
| 语种 | 英语 |
| DOI | arXiv:2411.16446 |
| 相关网址 | 查看原文 |
| 出处 | Arxiv |
| 收录类别 | PPRN.PPRN |
| WOS记录号 | PPRN:119389461 |
| WOS类目 | Computer Science, Software Engineering |
| 文献类型 | 预印本 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/471038 |
| 专题 | 生命科学与技术学院_特聘教授组_王佳伟组 |
| 通讯作者 | Wang, Jiawei |
| 作者单位 | 1.Univ Edinburgh, Edinburgh, Scotland 2.ShanghaiTech Univ, Shanghai, Peoples R China |
| 推荐引用方式 GB/T 7714 |
Wang, Jiawei,Cui, Zhiming,Li, Changjian. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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