Advancements in Talking Head Generation: A Comprehensive Review of Techniques, Metrics, and Challenges
Abstract
Abstract Talking Head Generation (THG) has emerged as a transformative technology in computer vision, synthesizing realistic human faces synchronized with audio, image, text, or video inputs. This paper systematically reviews THG methodologies and frameworks, categorizing approaches into 2D-based, 3D-based, Neural Radiance Fields (NeRF)-based, diffusion-based, parameter-driven, and other techniques. We explore the most effective approaches in THG, emphasizing training techniques that improve realism, identity preservation, and motion accuracy. THG has vast potential applications, including creating digital avatars, dubbing videos, enhancing virtual assistants, and improving video calls. However, challenges include needing large models, handling extreme head movements, maintaining language synchronicity, and ensuring smooth visuals persist. This review provides a comprehensive overview of current progress, identifies ongoing challenges, and suggests future research directions, including developing simpler and more adaptable models, enabling real-time processing on smaller devices, and creating ethical guidelines. By summarizing existing research and highlighting ongoing challenges, this overview offers valuable insights for anyone interested in the future of talking head technology. For the complete survey, code, and curated resource list, visit our GitHub repository: https://github.com/VineetKumarRakesh/thg.
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