HAAR: Text-Conditioned Generative Model of 3D Strand-based Human
  Hairstyles
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.
658268dad73d6402f7ab725e
Vanessa Sklyarova
claimed_verified
2023-12-20T10:32:11.382Z
65119f4c2f54c75d69da5df6
/avatars/2bf01b98136bd9fcb10b73e77353aaca.svg
Vanessa Sklyarova
user
VanessaSklyarova
658268dad73d6402f7ab725f
Egor Zakharov
admin_assigned
2023-12-20T09:37:05.293Z
648806533126307b34f8cf6c
/avatars/bca1155c1c34dadba81295f63f8e8bb9.svg
Egor Zakharov
user
eozakharov
658268dad73d6402f7ab7260
Otmar Hilliges
658268dad73d6402f7ab7261
Michael J. Black
admin_assigned
2023-12-20T09:37:20.235Z
649fbababa7a05e3626546c8
/avatars/a9aa4b4914649527094fc1b12779e014.svg
Michael J. Black
user
MichaelJBlack
658268dad73d6402f7ab7262
Justus Thies
HAAR: Text-Conditioned Generative Model of 3D Strand-based Human
  Hairstyles
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.
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.
latent diffusion model 3D hair strands VQA systems UV space