Synergizing Motion and Appearance: Multi-Scale Compensatory Codebooks for Talking Head Video Generation

Shuling Zhao, Fa-Ting Hong, Xiaoshui Huang, Dan Xu

Keywords: talking head video generation, motion transfer, codebook compensation

Abstract:

Talking head video generation aims to generate a realistic talking head video that
preserves the person’s identity from a source image and the motion from a driving
video. Despite the promising progress made in the field, it remains a challenging
and critical problem to generate videos with accurate poses and fine-grained facial
details simultaneously. Essentially, facial motion is often highly complex to model
precisely, and the one-shot source face image cannot provide sufficient appearance
guidance during generation due to dynamic pose changes. To tackle the problem, we propose to jointly learn motion and appearance codebooks and perform
multi-scale codebook compensation to effectively refine both the facial motion
conditions and appearance features for talking face image decoding. Specifically,
the designed multi-scale motion and appearance codebooks are learned simultaneously in a unified framework to store representative global facial motion flow
and appearance patterns. Then, we present a novel multi-scale motion and appearance compensation module, which utilizes a transformer-based codebook retrieval
strategy to query complementary information from the two codebooks for joint
motion and appearance compensation. The entire process produces motion flows
of greater flexibility and appearance features with fewer distortions across different scales, resulting in a high-quality talking head video generation framework.
Extensive experiments on various benchmarks validate the effectiveness of our
approach and demonstrate superior generation results from both qualitative and
quantitative perspectives when compared to state-of-the-art competitors.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 4330