Generating Animatable 3D Cartoon Faces from Single Portraits

• URL: http://arxiv.org/abs/2307.01468v1
• Date: Tue, 4 Jul 2023 04:12:50 GMT
• Title: Generating Animatable 3D Cartoon Faces from Single Portraits
• Authors: Chuanyu Pan, Guowei Yang, Taijiang Mu, and Yu-Kun Lai
• Abstract summary: We present a novel framework to generate animatable 3D cartoon faces from a single portrait image. We propose a two-stage reconstruction method to recover the 3D cartoon face with detailed texture. Finally, we propose a semantic preserving face rigging method based on manually created templates and deformation transfer.
• Score: 51.15618892675337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the booming of virtual reality (VR) technology, there is a growing need for customized 3D avatars. However, traditional methods for 3D avatar modeling are either time-consuming or fail to retain similarity to the person being modeled. We present a novel framework to generate animatable 3D cartoon faces from a single portrait image. We first transfer an input real-world portrait to a stylized cartoon image with a StyleGAN. Then we propose a two-stage reconstruction method to recover the 3D cartoon face with detailed texture, which first makes a coarse estimation based on template models, and then refines the model by non-rigid deformation under landmark supervision. Finally, we propose a semantic preserving face rigging method based on manually created templates and deformation transfer. Compared with prior arts, qualitative and quantitative results show that our method achieves better accuracy, aesthetics, and similarity criteria. Furthermore, we demonstrate the capability of real-time facial animation of our 3D model.

Related papers

• En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D Synthetic Data [36.51674664590734]
  We present En3D, an enhanced izable scheme for high-qualityd 3D human avatars. Unlike previous works that rely on scarce 3D datasets or limited 2D collections with imbalance viewing angles and pose priors, our approach aims to develop a zero-shot 3D capable of producing 3D humans.
  arXiv  Detail & Related papers  (2024-01-02T12:06:31Z)
• Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization [91.52882218901627]
  We propose a novel method for constructing implicit 3D morphable face models that are both generalizable and intuitive for editing. Our method improves upon photo-realism, geometry, and expression accuracy compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-04T17:58:40Z)
• AG3D: Learning to Generate 3D Avatars from 2D Image Collections [96.28021214088746]
  We propose a new adversarial generative model of realistic 3D people from 2D images. Our method captures shape and deformation of the body and loose clothing by adopting a holistic 3D generator. We experimentally find that our method outperforms previous 3D- and articulation-aware methods in terms of geometry and appearance.
  arXiv  Detail & Related papers  (2023-05-03T17:56:24Z)
• AvatarGen: A 3D Generative Model for Animatable Human Avatars [108.11137221845352]
  AvatarGen is an unsupervised generation of 3D-aware clothed humans with various appearances and controllable geometries. Our method can generate animatable 3D human avatars with high-quality appearance and geometry modeling. It is competent for many applications, e.g., single-view reconstruction, re-animation, and text-guided synthesis/editing.
  arXiv  Detail & Related papers  (2022-11-26T15:15:45Z)
• AvatarGen: a 3D Generative Model for Animatable Human Avatars [108.11137221845352]
  AvatarGen is the first method that enables not only non-rigid human generation with diverse appearance but also full control over poses and viewpoints. To model non-rigid dynamics, it introduces a deformation network to learn pose-dependent deformations in the canonical space. Our method can generate animatable human avatars with high-quality appearance and geometry modeling, significantly outperforming previous 3D GANs.
  arXiv  Detail & Related papers  (2022-08-01T01:27:02Z)
• 3D Cartoon Face Generation with Controllable Expressions from a Single GAN Image [142.047662926209]
  We generate 3D cartoon face shapes from single 2D GAN generated human faces. We manipulate latent codes to generate images with different poses and lighting, such that we can reconstruct the 3D cartoon face shapes.
  arXiv  Detail & Related papers  (2022-07-29T01:06:21Z)
• Building 3D Generative Models from Minimal Data [3.472931603805115]
  We show that our approach can be used to perform face recognition using only a single 3D template (one scan total, not one per person) We extend our model to a preliminary unsupervised learning framework that enables the learning of the distribution of 3D faces using one 3D template and a small number of 2D images.
  arXiv  Detail & Related papers  (2022-03-04T20:10:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.