StrandDesigner: Towards Practical Strand Generation with Sketch Guidance

2025-08-03

AI Summary

To address the challenge that existing hair modeling methods struggle to simultaneously achieve photorealism and fine-grained controllability under sketch guidance, this paper introduces the first sketch-based 3D hair strand generation model. Methodologically, we propose a multi-scale adaptive conditional diffusion architecture that integrates hierarchical latent encoding, learnable hair strand upsampling, and a cross-granularity consistency conditioning mechanism; a Transformer-based diffusion head enables end-to-end mapping from input sketches to high-fidelity 3D hair geometry. Our key contributions are: (1) the first sketch-driven framework for fine-grained, geometrically consistent, and user-friendly 3D hair strand synthesis; and (2) state-of-the-art performance across multiple benchmarks, significantly outperforming prior methods in photorealism, geometric detail fidelity, and sketch adherence.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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).

Problem

• Generates realistic hair strands from sketches
• Improves precision and user control in strand modeling
• Handles complex strand interactions and diverse patterns

Innovation

• Sketch-based strand generation model
• Learnable strand upsampling strategy
• Multi-scale adaptive conditioning mechanism