Talking Head Generation Through Generative Models and Cross-Modal Synthesis Techniques

Area of Science:

• Computer Vision and Deep Learning
• Speech Synthesis and Processing
• Human-Computer Interaction

Background:

• Talking Head Generation (THG) creates realistic animated human faces that speak and express emotions.
• THG integrates computer vision, deep learning, and speech synthesis to model audio-visual relationships.
• Applications span virtual assistants, avatars, dubbing, education, VR/AR, accessibility, and healthcare.

Purpose of the Study:

• To provide a comprehensive survey of the technological landscape of Talking Head Generation.
• To systematically review methodologies, datasets, evaluation metrics, and operational parameters.
• To highlight the impact of generative AI on THG advancements.

Main Methods:

• Outlines foundational methodologies: Generative Adversarial Networks (GANs), recurrent architectures, and attention-based models.
• Introduces a taxonomy for classifying THG approaches based on input modalities and generation goals.
• Reviews datasets, evaluation metrics (image quality, motion accuracy, synchronization, semantic fidelity), and operational parameters (latency, frame rate, resolution).

Main Results:

• Details the contributions of computer vision, speech processing, and human-robot interaction to THG.
• Analyzes the strengths and weaknesses of various datasets and evaluation metrics.
• Emphasizes the significant role of generative AI (GenAI) in enhancing THG realism and adaptability.

Conclusions:

• THG is a rapidly advancing field with broad applications and implications.
• Generative AI has substantially improved the capabilities and realism of THG systems.
• This survey offers a structured overview for researchers and developers in the THG domain.