MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation

Understand

In this work, we propose an ID-preserving talking head generation framework, which advances previous methods in two aspects.

• First, as opposed to interpolating from sparse flow, we claim that dense landmarks are crucial to achieving accurate geometry-aware flow fields.
• Second, inspired by face-swapping methods, we adaptively fuse the source identity during synthesis, so that the network better preserves the key characteristics of the image portrait.
• Although the proposed model surpasses prior generation fidelity on established benchmarks, to further make the talking head generation qualified for real usage, personalized fine-tuning is usually needed.