GenCA: A Text-conditioned Generative Model for Realistic and Drivable Codec Avatars

Understand

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.