HAAR: Text-Conditioned Generative Model of 3D Strand-based Human
  Hairstyles
Vanessa Sklyarova Egor Zakharov Otmar Hilliges Michael J. Black Justus Thies
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.