StrandDesigner: Towards Practical Strand Generation with Sketch Guidance
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866911088656777216 |
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| author | Zhang, Na Li, Moran Xu, Chengming Feng, Han Hu, Xiaobin Zhang, Jiangning Cao, Weijian Wang, Chengjie Fu, Yanwei |
| author_facet | Zhang, Na Li, Moran Xu, Chengming Feng, Han Hu, Xiaobin Zhang, Jiangning Cao, Weijian Wang, Chengjie Fu, Yanwei |
| contents | Realistic hair strand generation is crucial for applications like computer graphics and virtual reality. While diffusion models can generate hairstyles from text or images, these inputs lack precision and user-friendliness. Instead, we propose the first sketch-based strand generation model, which offers finer control while remaining user-friendly. Our framework tackles key challenges, such as modeling complex strand interactions and diverse sketch patterns, through two main innovations: a learnable strand upsampling strategy that encodes 3D strands into multi-scale latent spaces, and a multi-scale adaptive conditioning mechanism using a transformer with diffusion heads to ensure consistency across granularity levels. Experiments on several benchmark datasets show our method outperforms existing approaches in realism and precision. Qualitative results further confirm its effectiveness. Code will be released at [GitHub](https://github.com/fighting-Zhang/StrandDesigner). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01650 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | StrandDesigner: Towards Practical Strand Generation with Sketch Guidance Zhang, Na Li, Moran Xu, Chengming Feng, Han Hu, Xiaobin Zhang, Jiangning Cao, Weijian Wang, Chengjie Fu, Yanwei Computer Vision and Pattern Recognition Realistic hair strand generation is crucial for applications like computer graphics and virtual reality. While diffusion models can generate hairstyles from text or images, these inputs lack precision and user-friendliness. Instead, we propose the first sketch-based strand generation model, which offers finer control while remaining user-friendly. Our framework tackles key challenges, such as modeling complex strand interactions and diverse sketch patterns, through two main innovations: a learnable strand upsampling strategy that encodes 3D strands into multi-scale latent spaces, and a multi-scale adaptive conditioning mechanism using a transformer with diffusion heads to ensure consistency across granularity levels. Experiments on several benchmark datasets show our method outperforms existing approaches in realism and precision. Qualitative results further confirm its effectiveness. Code will be released at [GitHub](https://github.com/fighting-Zhang/StrandDesigner). |
| title | StrandDesigner: Towards Practical Strand Generation with Sketch Guidance |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.01650 |