标题： StrandDesigner：基于草图指导的实用线段生成方法 显示英文标题

标题： StrandDesigner: Towards Practical Strand Generation with Sketch Guidance

摘要： 真实发丝生成对于计算机图形学和虚拟现实等应用至关重要。 虽然扩散模型可以从文本或图像生成发型，但这些输入缺乏精确性和用户友好性。 相反，我们提出了第一个基于草图的发丝生成模型，它在保持用户友好性的同时提供了更精细的控制。 我们的框架通过两项主要创新来解决关键挑战，例如建模复杂的发丝交互和多样的草图模式：一种可学习的发丝上采样策略，将3D发丝编码到多尺度潜在空间中，以及一种使用带有扩散头的变压器的多尺度自适应条件机制，以确保不同粒度级别的一致性。 在多个基准数据集上的实验表明，我们的方法在真实感和精度方面优于现有方法。 定性结果进一步证实了其有效性。 代码将在[GitHub](https://github.com/fighting-Zhang/StrandDesigner)上发布。 显示英文摘要

摘要： 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).