StrandDesigner: Towards Practical Strand Generation with Sketch Guidance
Authors: name: Na Zhang, name: Moran Li, name: Chengming Xu, name: Han Feng, name: Xiaobin Hu, name: Jiangning Zhang, name: Weijian Cao, name: Chengjie Wang, name: Yanwei Fu

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).