From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications

2023-08-30 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

This comprehensive survey, "From Pixels to Portraits," reviews the rapidly advancing field of talking head generation, which has evolved from landmark- and GAN-based methods to sophisticated diffusion models, neural rendering, 3D-aware avatars, and foundation-model-assisted systems. The survey organizes the literature into four main families: image-driven, audio-driven, video-driven, and 3D/neural-rendering-based approaches, detailing their technical ideas, methods, strengths, limitations, datasets, and evaluation practices. It also analyzes the gap between quantitative metrics and perceptual quality, compares publicly available models on inference time, memory, and visual quality, and examines emerging trends like diffusion-based generation, controllable emotional expression, and real-time deployment. Furthermore, the review addresses critical challenges including robust evaluation, identity preservation, lip synchronization, temporal consistency, demographic fairness, computational efficiency, and responsible use, such as provenance and deepfake detection.

Key takeaway

For AI Scientists and Machine Learning Engineers developing talking head systems, you must critically assess techniques beyond reported metrics, considering real-world perceptual quality, computational costs, and ethical implications. Prioritize robust evaluation, identity preservation, and temporal consistency in your models. Implement provenance and watermarking early to mitigate misuse risks and ensure responsible deployment.

Key insights

Talking head generation rapidly advances, but navigating its diverse techniques, evaluation gaps, and ethical risks is complex.

Principles

• Techniques vary by input, control, and cost.
• Quantitative metrics often misalign with perception.
• Responsible use requires provenance and detection.

Method

The survey categorizes talking head generation into image-driven, audio-driven, video-driven, and 3D/neural-rendering approaches, detailing each family's ideas, methods, and evaluation.

In practice

• Compare models on inference time and memory.
• Prioritize identity preservation and lip sync.
• Implement watermarking for deepfake detection.

Topics

• Talking Head Generation
• Diffusion Models
• Neural Rendering
• Deepfake Detection
• Generative AI
• Video Synthesis

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.