GenCA: A Text-conditioned Generative Model for Realistic and Drivable
  Codec Avatars
Photo-realistic and controllable 3D avatars are crucial for various
applications such as virtual and mixed reality (VR/MR), telepresence, gaming,
and film production. Traditional methods for avatar creation often involve
time-consuming scanning and reconstruction processes for each avatar, which
limits their scalability. Furthermore, these methods do not offer the
flexibility to sample new identities or modify existing ones. On the other
hand, by learning a strong prior from data, generative models provide a
promising alternative to traditional reconstruction methods, easing the time
constraints for both data capture and processing. Additionally, generative
methods enable downstream applications beyond reconstruction, such as editing
and stylization. Nonetheless, the research on generative 3D avatars is still in
its infancy, and therefore current methods still have limitations such as
creating static avatars, lacking photo-realism, having incomplete facial
details, or having limited drivability. To address this, we propose a
text-conditioned generative model that can generate photo-realistic facial
avatars of diverse identities, with more complete details like hair, eyes and
mouth interior, and which can be driven through a powerful non-parametric
latent expression space. Specifically, we integrate the generative and editing
capabilities of latent diffusion models with a strong prior model for avatar
expression driving.
  Our model can generate and control high-fidelity avatars, even those
out-of-distribution. We also highlight its potential for downstream
applications, including avatar editing and single-shot avatar reconstruction.
66ce87b28ec9fda2cf61078d
Keqiang Sun
admin_assigned
2024-08-28T08:16:15.959Z
645ca9f3a19f3e64bbed7534
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Keqiang Sun
user
kqsun
66ce87b28ec9fda2cf61078e
Amin Jourabloo
66ce87b28ec9fda2cf61078f
Riddhish Bhalodia
66ce87b28ec9fda2cf610790
Moustafa Meshry
66ce87b28ec9fda2cf610791
Yu Rong
claimed_verified
2025-07-31T21:01:32.065Z
642eecbf9b2484d7d8526781
https://cdn-avatars.huggingface.co/v1/production/uploads/642eecbf9b2484d7d8526781/4IvGbd66s49Wx5pZyZGHA.png
Yu Rong
user
Swrooy
66ce87b28ec9fda2cf610792
Zhengyu Yang
66ce87b28ec9fda2cf610793
Thu Nguyen-Phuoc
admin_assigned
2024-08-28T08:17:42.507Z
6304b663c2f4f2d4929ad200
/avatars/805d4dab512fc53d8bd6bcb60db3821b.svg
Thu Nguyen-Phuoc
user
TNP23
66ce87b28ec9fda2cf610794
Christian Haene
66ce87b28ec9fda2cf610795
Jiu Xu
66ce87b28ec9fda2cf610796
Sam Johnson
66ce87b28ec9fda2cf610797
Hongsheng Li
admin_assigned
2024-08-28T08:18:33.911Z
65c04e9c27a5fdca81abcbd9
/avatars/12a155683c824fa23da4a9e2bed4f64e.svg
Hongsheng LI
user
hsli-cuhk
66ce87b28ec9fda2cf610798
Sofien Bouaziz
GenCA: A Text-conditioned Generative Model for Realistic and Drivable
  Codec Avatars
Photo-realistic and controllable 3D avatars are crucial for various
applications such as virtual and mixed reality (VR/MR), telepresence, gaming,
and film production. Traditional methods for avatar creation often involve
time-consuming scanning and reconstruction processes for each avatar, which
limits their scalability. Furthermore, these methods do not offer the
flexibility to sample new identities or modify existing ones. On the other
hand, by learning a strong prior from data, generative models provide a
promising alternative to traditional reconstruction methods, easing the time
constraints for both data capture and processing. Additionally, generative
methods enable downstream applications beyond reconstruction, such as editing
and stylization. Nonetheless, the research on generative 3D avatars is still in
its infancy, and therefore current methods still have limitations such as
creating static avatars, lacking photo-realism, having incomplete facial
details, or having limited drivability. To address this, we propose a
text-conditioned generative model that can generate photo-realistic facial
avatars of diverse identities, with more complete details like hair, eyes and
mouth interior, and which can be driven through a powerful non-parametric
latent expression space. Specifically, we integrate the generative and editing
capabilities of latent diffusion models with a strong prior model for avatar
expression driving.
  Our model can generate and control high-fidelity avatars, even those
out-of-distribution. We also highlight its potential for downstream
applications, including avatar editing and single-shot avatar reconstruction.
A text-conditioned generative model using latent diffusion integrates a strong prior model for avatar expression, enabling the generation and control of high-fidelity avatars with detailed facial features.
generative 3D avatars text-conditioned generative model latent diffusion models latent expression space avatar editing single-shot avatar reconstruction