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

• URL: http://arxiv.org/abs/2212.08062v3
• Date: Mon, 27 Mar 2023 02:16:13 GMT
• Title: MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation
• Authors: Bowen Zhang, Chenyang Qi, Pan Zhang, Bo Zhang, HsiangTao Wu, Dong Chen, Qifeng Chen, Yong Wang, Fang Wen
• Abstract summary: We propose an ID-preserving talking head generation framework. We claim that dense landmarks are crucial to achieving accurate geometry-aware flow fields. We adaptively fuse the source identity during synthesis, so that the network better preserves the key characteristics of the image portrait.
• Score: 57.060828009199646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. However, this process is rather computationally demanding that is unaffordable to standard users. To solve this, we propose a fast adaptation model using a meta-learning approach. The learned model can be adapted to a high-quality personalized model as fast as 30 seconds. Last but not the least, a spatial-temporal enhancement module is proposed to improve the fine details while ensuring temporal coherency. Extensive experiments prove the significant superiority of our approach over the state of the arts in both one-shot and personalized settings.

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