MaintaAvatar: A Maintainable Avatar Based on Neural Radiance Fields by Continual Learning
This addresses a practical challenge in computer vision for creating maintainable virtual avatars in real-world scenarios where subject data evolves, though it is incremental as it builds on existing NeRF and continual learning methods.
The paper tackles the problem of updating a human avatar based on Neural Radiance Fields (NeRF) as appearances and poses change over time, while avoiding catastrophic forgetting of old appearances, and achieves this through a proposed model that requires limited data for fine-tuning.
The generation of a virtual digital avatar is a crucial research topic in the field of computer vision. Many existing works utilize Neural Radiance Fields (NeRF) to address this issue and have achieved impressive results. However, previous works assume the images of the training person are available and fixed while the appearances and poses of a subject could constantly change and increase in real-world scenarios. How to update the human avatar but also maintain the ability to render the old appearance of the person is a practical challenge. One trivial solution is to combine the existing virtual avatar models based on NeRF with continual learning methods. However, there are some critical issues in this approach: learning new appearances and poses can cause the model to forget past information, which in turn leads to a degradation in the rendering quality of past appearances, especially color bleeding issues, and incorrect human body poses. In this work, we propose a maintainable avatar (MaintaAvatar) based on neural radiance fields by continual learning, which resolves the issues by utilizing a Global-Local Joint Storage Module and a Pose Distillation Module. Overall, our model requires only limited data collection to quickly fine-tune the model while avoiding catastrophic forgetting, thus achieving a maintainable virtual avatar. The experimental results validate the effectiveness of our MaintaAvatar model.