LeGO: Leveraging a Surface Deformation Network for Animatable Stylized Face Generation with One Example

Concepts de base

Proposing a method for highly stylized 3D face models with desired topology using surface deformation network and paired exemplar.

Résumé

The article introduces a novel method, LeGO, for generating highly stylized 3D face models with desired topologies. It focuses on training a surface deformation network with 3DMM and translating its domain to the target style using a paired exemplar. The method achieves stylization of 3D face mesh by mimicking the style of the target through differentiable renderer and directional CLIP losses. The Mesh Agnostic Encoder (MAGE) is utilized during inference to enable mesh-agnostic stylization for input with diverse topologies. The results demonstrate successful production of highly stylized face meshes according to given styles and desired topologies, suitable for applications like image-based stylized avatar generation and facial animation.
Introduction:

Recent advances in 3D face stylization have made significant strides in few to zero-shot settings.
Existing methods based on statistical 3D Morphable Models (3DMM) lack sufficient variation for practical applications.
Key Elements for Stylized Avatar Creation:

Avatar creation in a desired topology compatible with conventional CG pipelines.
Extending stylization capabilities beyond 3DMM.
Generating animatable stylized avatars using blend shapes.

Proposed Method:

Leverages surface deformation network trained with FLAME decoder to achieve highly stylized 3D face models.
Utilizes directional CLIP-based domain adaptation method for identity preservation while reflecting desired style.
Hierarchical rendering scheme captures local and global facial features effectively during training and inference stages.
Related Work:

Various methods like GANs, DMs, text-based geometry deformation, and surface deformation have been explored for generating high-quality and diverse stylized 3D faces.

Stats

"During fine-tuning, we employ a directional CLIP-based domain adaptation method."
"We also demonstrate example applications of our method including image-based stylized avatar generation."
"MAGE is composed of pre-trained encoders from Neural Face Rigging (NFR) and latent mapping networks."
"The resulting stylized face model can be animated by commonly used 3DMM blend shapes."

Citations

Idées clés tirées de

LeGO

by Soyeon Yoon,... à arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15227.pdf

Questions plus approfondies

How does the proposed method compare to existing techniques in terms of efficiency

The proposed method demonstrates efficiency in generating stylized 3D face meshes with diverse topologies and styles. Compared to existing techniques, the method leverages a surface deformation network trained with FLAME and utilizes a Mesh Agnostic Encoder (MAGE) for mesh agnostic stylization. This approach allows for the generation of highly stylized faces with desired topologies while ensuring consistency across different deformation target mesh representations. Additionally, the hierarchical rendering scheme captures local and global facial features effectively during training, enhancing identity preservation and style adherence.

What are the potential limitations or challenges faced when applying this method in real-world scenarios

When applying this method in real-world scenarios, there are potential limitations or challenges to consider. One challenge could be the computational resources required for training and fine-tuning the surface deformation network with paired exemplars. The two-stage process involving template replacement at inference may introduce additional complexity and time overheads in practical applications. Ensuring seamless integration into existing graphics pipelines and animation tools may also pose challenges, especially when dealing with diverse mesh topologies.

How might advancements in this field impact industries beyond entertainment

Advancements in this field can have significant impacts beyond entertainment industries. For example:

Healthcare: Stylized 3D avatars can be used for medical simulations, patient education, or virtual consultations.
Fashion: Virtual try-on experiences using stylized avatars can enhance online shopping platforms.
Education: Interactive learning environments utilizing animated avatars can improve engagement in e-learning platforms.
Marketing: Personalized advertising campaigns featuring stylized avatars can increase customer engagement.
Virtual Reality/Augmented Reality: Stylized avatars can enhance immersive experiences in VR/AR applications across various industries.
These advancements open up opportunities for innovative solutions that leverage animatable stylized faces for a wide range of practical applications beyond traditional entertainment contexts.