Personalized Generative 3D Avatars with Composable Facial Attributes

Conceitos Básicos

PEGASUS, a method for constructing a personalized generative 3D face avatar from monocular video sources, enables disentangled controls to selectively alter facial attributes while preserving the identity.

Resumo

The paper presents PEGASUS, a method for building a personalized generative 3D face avatar from monocular video sources. The key highlights are:

1. PEGASUS enables disentangled controls to selectively alter facial attributes (e.g., hair, nose, accessories) while preserving the identity of the target person.
2. The approach consists of two stages: (1) synthetic database generation by borrowing facial attributes from diverse identities, and (2) constructing a personalized generative 3D avatar model.
3. The synthetic database is created by swapping facial parts (e.g., hair, nose) of the target person with those from other monocular videos, allowing the model to learn continuous variations of facial attributes.
4. The personalized generative 3D avatar model is trained on the synthetic database, enabling selective modification of facial attributes through disentangled latent codes.
5. An alternative "zero-shot" approach is introduced to efficiently achieve the same goal of a personalized generative avatar by fusing the controlled attribute parts from a pre-trained model and the remaining parts of a new target identity.
6. Extensive experiments demonstrate the effectiveness of the proposed methods in preserving identity while achieving high realism and controllability of facial attributes.

Estatísticas

"Building a personalized 3D avatar for representing an individual in virtual spaces can bring significant advancements in the field of AR/VR and applications within the metaverse."
"Recent technologies make it possible to build high-quality 3D face avatars for general users from monocular video inputs only."
"Our personalized generative 3D avatar is constructed from the monocular video of the target individual."

Citações

"In contrast to the previous work [59, 60], our 3D avatar enables compositional controls of facial attributes, where users can make alterations for desired facial attributes such as hair, nose, or accessories, as shown in Fig. 1, while preserving the identity of the target person."
"Built with diverse part-swapped videos, our generative 3D avatar, PEGASUS, can preserve high-quality details for the target individuals, while equipped with the generative power to selectively alter each facial part."

Principais Insights Extraídos De

PEGASUS

by Hyunsoo Cha,... às arxiv.org 04-03-2024

https://arxiv.org/pdf/2402.10636.pdf

Perguntas Mais Profundas

How can the proposed method be extended to handle more diverse facial attributes beyond the ones demonstrated, such as accessories, facial expressions, or even full body avatars?

The proposed method can be extended to handle more diverse facial attributes by incorporating additional modules or components into the generative model. Here are some ways to achieve this:

Accessories: To handle accessories like glasses, earrings, or headwear, the model can be trained on a dataset that includes individuals wearing different types of accessories. By introducing specific latent codes for each accessory type, the model can learn to generate avatars with various accessories.

Facial Expressions: For handling facial expressions, the model can be trained on a dataset that includes individuals displaying a wide range of expressions. By incorporating expression-specific latent codes, the model can learn to generate avatars with different facial expressions, allowing users to customize the emotional state of their avatars.

Full Body Avatars: To extend the method to generate full-body avatars, the model can be modified to include additional parameters for body shape, posture, and clothing. By training the model on a dataset that includes full-body images, the generative model can learn to create personalized full-body avatars with diverse attributes.

By incorporating these additional features and training the model on more comprehensive datasets, the proposed method can be extended to handle a wider range of facial attributes, expressions, accessories, and even full-body avatars, providing users with more flexibility and customization options.

How can the proposed method be extended to handle more diverse facial attributes beyond the ones demonstrated, such as accessories, facial expressions, or even full body avatars?

The proposed method can be extended to handle more diverse facial attributes by incorporating additional modules or components into the generative model. Here are some ways to achieve this:

Accessories: To handle accessories like glasses, earrings, or headwear, the model can be trained on a dataset that includes individuals wearing different types of accessories. By introducing specific latent codes for each accessory type, the model can learn to generate avatars with various accessories.

Facial Expressions: For handling facial expressions, the model can be trained on a dataset that includes individuals displaying a wide range of expressions. By incorporating expression-specific latent codes, the model can learn to generate avatars with different facial expressions, allowing users to customize the emotional state of their avatars.

Full Body Avatars: To extend the method to generate full-body avatars, the model can be modified to include additional parameters for body shape, posture, and clothing. By training the model on a dataset that includes full-body images, the generative model can learn to create personalized full-body avatars with diverse attributes.

By incorporating these additional features and training the model on more comprehensive datasets, the proposed method can be extended to handle a wider range of facial attributes, expressions, accessories, and even full-body avatars, providing users with more flexibility and customization options.

What are the potential limitations or failure cases of the part-swapping approach used to generate the synthetic database, and how could these be addressed in future work?

The part-swapping approach used to generate the synthetic database in the PEGASUS framework may have some limitations and potential failure cases:

Artifacts and Inconsistencies: One limitation is the possibility of artifacts or inconsistencies in the synthesized images when swapping facial parts from different individuals. This can result in unnatural-looking avatars with mismatched features.

Identity Preservation: There may be challenges in preserving the identity of the target individual while swapping facial attributes. If the swapping process is not accurate, it could lead to a loss of identity in the generated avatars.

Limited Attribute Variation: The part-swapping approach may have limitations in handling a wide range of facial attributes beyond the ones demonstrated. It may struggle with more complex attributes or combinations of attributes.

To address these limitations and potential failure cases, future work could focus on:

Improved Alignment Techniques: Developing more advanced alignment techniques to ensure accurate swapping of facial parts and better preservation of the target individual's identity.

Fine-tuning and Refinement: Implementing post-processing techniques to refine the synthesized images and reduce artifacts or inconsistencies in the generated avatars.

Dataset Diversity: Increasing the diversity of the training dataset to include a broader range of facial attributes and variations, enabling the model to learn more robust representations for attribute swapping.

By addressing these aspects and incorporating advanced techniques, the part-swapping approach can be enhanced to overcome its limitations and produce more realistic and identity-preserving avatars.

Given the advancements in text-to-image and text-to-3D generation, how could the PEGASUS framework be combined with language-guided avatar editing to enable even more flexible and expressive personalized avatars?

Combining the PEGASUS framework with language-guided avatar editing can enhance the flexibility and expressiveness of personalized avatars. Here's how this integration could be achieved:

Language-Guided Attribute Editing: Integrate natural language processing (NLP) models to interpret textual descriptions of desired avatar attributes. Users can input text descriptions of specific attributes or modifications they want for their avatars, such as "long curly hair" or "smiling expression."

Attribute Synthesis: Use the language input to guide the generation of attribute-specific latent codes in the PEGASUS framework. The model can interpret the textual descriptions and generate corresponding latent codes for the desired attributes, enabling users to customize their avatars based on text inputs.

Interactive Avatar Editing: Implement an interactive interface where users can input text descriptions and see real-time updates of their avatars based on the language-guided edits. This interactive editing feature can provide users with immediate feedback on how their textual descriptions translate into visual changes in the avatar.

Multi-Modal Fusion: Explore multi-modal fusion techniques to combine textual descriptions with visual inputs for more accurate and context-aware avatar editing. By leveraging both text and image modalities, the model can better understand user preferences and generate personalized avatars that align with the user's descriptions.

User Feedback Loop: Incorporate a feedback loop where users can provide input on the generated avatars and make further adjustments based on the visual outputs. This iterative process can enhance user satisfaction and customization options.

By integrating language-guided avatar editing into the PEGASUS framework, users can enjoy a more intuitive and interactive experience in customizing their avatars. This combination of text-based inputs and generative modeling can lead to more flexible, expressive, and user-centric avatar creation tools.