CartoonCodec: Generative talking face video coding with cartoon-style customization

The rapid growth of video-based social applications has intensified the demand for efficient transmission and personalized cartoon-style customization. Existing solutions typically apply talking face video and text codecs, followed by cartoon-style control algorithms, which often result in unsatisfactory compression performance and high inference latency. In this paper, we propose CartoonCodec, an efficient generative framework that unifies face video coding and cartoon-style control into an end-to-end process. Our framework encodes facial video sequences into compact motion feature representations, which are transmitted together with compressed text prompts. To integrate control into the coding process while ensuring efficient compression, a text-guided adaptive layer selection mechanism relying on the compact representations is introduced to dynamically select and optimize the most influential layers in the generators. Furthermore, to facilitate stylization over the decoupled spaces, we propose a self-supervised domain stylization training strategy that constructs both multimodal and unimodal data pairs, enabling the use of diverse loss functions. Extensive experiments demonstrate that CartoonCodec outperforms baselines in compression efficiency for video reconstruction and cartoon-style control tasks while maintaining competitive inference efficiency. CartoonCodec provides key insights for advancing face video communication with cartoon-style customization. The project page can be found at https://github.com/xiaonae/CartoonCodec/tree/main.
© 2026 The Author(s).