title: HAAR: Text-Conditioned Generative Model of 3D Strand-based Human
    Hairstyles
summary: 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.
paper_authors._id: 658268dad73d6402f7ab725e
paper_authors.name: Vanessa Sklyarova
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-20T10:32:11.382Z
paper_authors._id: 65119f4c2f54c75d69da5df6
paper_authors.avatarUrl: /avatars/2bf01b98136bd9fcb10b73e77353aaca.svg
paper_authors.fullname: Vanessa Sklyarova
paper_authors.type: user
paper_authors.user: VanessaSklyarova
paper_authors._id: 658268dad73d6402f7ab725f
paper_authors.name: Egor Zakharov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-20T09:37:05.293Z
paper_authors._id: 648806533126307b34f8cf6c
paper_authors.avatarUrl: /avatars/bca1155c1c34dadba81295f63f8e8bb9.svg
paper_authors.fullname: Egor Zakharov
paper_authors.type: user
paper_authors.user: eozakharov
paper_authors._id: 658268dad73d6402f7ab7260
paper_authors.name: Otmar Hilliges
paper_authors._id: 658268dad73d6402f7ab7261
paper_authors.name: Michael J. Black
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-20T09:37:20.235Z
paper_authors._id: 649fbababa7a05e3626546c8
paper_authors.avatarUrl: /avatars/a9aa4b4914649527094fc1b12779e014.svg
paper_authors.fullname: Michael J. Black
paper_authors.type: user
paper_authors.user: MichaelJBlack
paper_authors._id: 658268dad73d6402f7ab7262
paper_authors.name: Justus Thies
paper_title: HAAR: Text-Conditioned Generative Model of 3D Strand-based Human
    Hairstyles
paper_summary: 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.
paper_ai_summary: HAAR is a text-guided generative model using 3D hair strands and latent diffusion trained in UV space, addressing limitations of 2D-based 3D hairstyle generation.
paper_ai_keywords: latent diffusion model 3D hair strands VQA systems UV space