A Comprehensive Review and Taxonomy of Audio-Visual Synchronization Techniques for Realistic Speech Animation
원문 초록
In many applications, synchronizing audio with visuals is crucial, such as in creating graphic animations for films or games, translating movie audio into different languages, and developing metaverse applications. This review explores various methodologies for achieving realistic facial animations from audio inputs, highlighting generative and adaptive models. Addressing challenges like model training costs, dataset availability, and silent moment distributions in audio data, it presents innovative solutions to enhance performance and realism. The research also introduces a new taxonomy to categorize audio-visual synchronization methods based on logistical aspects, advancing the capabilities of virtual assistants, gaming, and interactive digital media.
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CoGenAV: Versatile Audio-Visual Representation Learning via Contrastive-Generative Synchronization
MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
MuseTalk: Real-Time High-Fidelity Video Dubbing via Spatio-Temporal Sampling
Explicit Correlation Learning for Generalizable Cross-Modal Deepfake Detection
PEAVS: Perceptual Evaluation of Audio-Visual Synchrony Grounded in Viewers' Opinion Scores
Synchformer: Efficient Synchronization from Sparse Cues
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