Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles

Vanessa Sklyarova, Egor Zakharov, Otmar Hilliges, Michael J. Black, Justus Thies; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 4703-4712

We present HAAR a new strand-based generative model for 3D human hairstyles. Specifically based on textual inputs HAAR produces 3D hairstyles that could be used as production-level assets in modern computer graphics engines. Current AI-based generative models take advantage of powerful 2D priors to reconstruct 3D content in the form of point clouds meshes or volumetric functions. However by using the 2D priors they are intrinsically limited to only recovering the visual parts. Highly occluded hair structures can not be reconstructed with those methods and they only model the "outer shell" which is not ready to be used in physics-based rendering or simulation pipelines. In contrast we propose a first text-guided generative method that uses 3D hair strands as an underlying representation. Leveraging 2D visual question-answering (VQA) systems we automatically annotate synthetic hair models that are generated from a small set of artist-created hairstyles. This allows us to train a latent diffusion model that operates in a common hairstyle UV space. In qualitative and quantitative studies we demonstrate the capabilities of the proposed model and compare it to existing hairstyle generation approaches. For results please refer to our project page https://haar.is.tue.mpg.de/.