SyncTalk++: High-Fidelity and Efficient Synchronized Talking Heads Synthesis Using Gaussian Splatting

Achieving high synchronization in the synthesis of realistic, speech-driven talking head videos presents a significant challenge. A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses. The absence of these synchronizations is a fundamental flaw, leading to unrealistic results. To address the critical issue of synchronization, identified as the "devil" in creating realistic talking heads, we introduce SyncTalk++, which features a Dynamic Portrait Renderer with Gaussian Splatting to ensure consistent subject identity preservation and a Face-Sync Controller that aligns lip movements with speech while innovatively using a 3D facial blendshape model to reconstruct accurate facial expressions. To ensure natural head movements, we propose a Head-Sync Stabilizer, which optimizes head poses for greater stability. Additionally, SyncTalk++ enhances robustness to out-of-distribution (OOD) audio by incorporating an Expression Generator and a Torso Restorer, which generate speech-matched facial expressions and seamless torso regions. Our approach maintains consistency and continuity in visual details across frames and significantly improves rendering speed and quality, achieving up to 101 frames per second. Extensive experiments and user studies demonstrate that SyncTalk++ outperforms state-of-the-art methods in synchronization and realism.

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Publication Details

Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
2025-01-01
DOI
https://doi.org/10.1109/tpami.2025.3630057
Citations
2
Primary Topic
Face recognition and analysis
Type
article
Field-Weighted Citation Impact
2.55
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article

SyncTalk++: High-Fidelity and Efficient Synchronized Talking Heads Synthesis Using Gaussian Splatting

X. M. Zhang, Jiao Ma, Zhaoxin Fan, Hui Tian et al.
2 citations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition and analysis
2.55
article

SyncTalk++: High-Fidelity and Efficient Synchronized Talking Heads Synthesis Using Gaussian Splatting

X. M. Zhang, Jiao Ma, Zhaoxin Fan, Hui Tian, Wentao Hu, J. He, Ziqiao Peng, Hongyan Liu, Hao Zhao, Xiangyu Zhu
article en
2 citations

Abstract

Achieving high synchronization in the synthesis of realistic, speech-driven talking head videos presents a significant challenge. A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses. The absence of these synchronizations is a fundamental flaw, leading to unrealistic results. To address the critical issue of synchronization, identified as the "devil" in creating realistic talking heads, we introduce SyncTalk++, which features a Dynamic Portrait Renderer with Gaussian Splatting to ensure consistent subject identity preservation and a Face-Sync Controller that aligns lip movements with speech while innovatively using a 3D facial blendshape model to reconstruct accurate facial expressions. To ensure natural head movements, we propose a Head-Sync Stabilizer, which optimizes head poses for greater stability. Additionally, SyncTalk++ enhances robustness to out-of-distribution (OOD) audio by incorporating an Expression Generator and a Torso Restorer, which generate speech-matched facial expressions and seamless torso regions. Our approach maintains consistency and continuity in visual details across frames and significantly improves rendering speed and quality, achieving up to 101 frames per second. Extensive experiments and user studies demonstrate that SyncTalk++ outperforms state-of-the-art methods in synchronization and realism.

IEEE Transactions on Pattern Analysis and Machine IntelligenceVol. PP
Beijing University of Posts and Telecommunications (CN), Chinese Academy of Sciences (CN), Institute of Automation (CN), Aerospace Information Research Institute (CN), Beijing Advanced Sciences and Innovation Center (CN), Renmin University of China (CN), Tsinghua University (CN)
Openalex Percentile: Top 8%
Face recognition and analysis
2.55
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