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Lip-syncing technology, integral to fields ranging from digital media production to assistive technologies, has seen substantial advancements with the integration of deep learning models, particularly Generative Adversarial Networks (GANs). This literature review investigates the evolution, methodologies, and impact of recent lip-syncing solutions, with a detailed focus on the Wav2Lip model and various GAN architectures. Wav2Lip, recognized for its high accuracy and robustness across diverse speaking styles and facial conditions, represents a significant leap in achieving realistic and adaptable lip synchronization, even under challenging conditions such as cross-language dubbing and expressive variations.
This survey synthesizes findings from numerous research papers, examining the progression from early rule-based and phonetic alignment techniques to the sophisticated, data-driven GAN models that now dominate the field. Key topics include model architectures, training methodologies, loss functions, and evaluation metrics that have collectively advanced the state of the art. The report also explores the challenges inherent in lip-syncing, such as handling occlusions, preserving speaker identity, and achieving real-time performance, as well as potential solutions proposed in the literature. Furthermore, it assesses the applications and limitations of current models, considering the ethical and practical implications of increasingly realistic synthesized speech and lip movements.
"Advancements in Lip-Syncing Technology: A Comprehensive Look of GAN-Based Approaches for Audio-Visual Synchronization", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 1, page no.a179-a185, January-2025, Available :http://www.ijrti.org/papers/IJRTI2501026.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator