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Audio-driven Talking Face Generation with Stabilized Synchronization Loss

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arxiv 2307.09368 v3 pith:ZNOAKPXS submitted 2023-07-18 cs.CV

Dogucan Yaman , Fevziye Irem Eyiokur , Leonard Bärmann , Hazim Kemal Ekenel , Alexander Waibel This is my paper
classification cs.CV
keywords synchronizationlossvisualidentityissuesqualityreferencecaused
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Talking face generation aims to create realistic videos with accurate lip synchronization and high visual quality, using given audio and reference video while preserving identity and visual characteristics. In this paper, we start by identifying several issues with existing synchronization learning methods. These involve unstable training, lip synchronization, and visual quality issues caused by lip-sync loss, SyncNet, and lip leaking from the identity reference. To address these issues, we first tackle the lip leaking problem by introducing a silent-lip generator, which changes the lips of the identity reference to alleviate leakage. We then introduce stabilized synchronization loss and AVSyncNet to overcome problems caused by lip-sync loss and SyncNet. Experiments show that our model outperforms state-of-the-art methods in both visual quality and lip synchronization. Comprehensive ablation studies further validate our individual contributions and their cohesive effects.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Mask-Free Audio-driven Talking Face Generation for Enhanced Visual Quality and Identity Preservation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    MF-Talk, a mask-free and identity-reference-free three-stage pipeline, improves visual quality and identity preservation in talking-face generation while remaining competitive on lip-sync.

  2. FaceEditTalker: Controllable Talking Head Generation with Facial Attribute Editing

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A single framework can edit predefined facial attributes in audio-synchronized talking head videos while preserving identity and lip-sync quality.

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