说话人头部合成的综合分类与分析：肖像生成、驱动机制与编辑技术
A Comprehensive Taxonomy and Analysis of Talking Head Synthesis: Techniques for Portrait Generation, Driving Mechanisms, and Editing
AI总结：
本综述系统梳理说话人头部合成技术，将其划分为肖像生成、驱动机制、编辑技术三大领域，分析里程碑研究的优劣，整理数据集并多指标评估现有方法，探讨应用场景与未来方向，为后续研究提供框架与数据支撑。
摘要：
说话人头部合成是一种由特定内容驱动、从静态图像生成肖像视频的先进方法，在虚拟现实、增强现实和游戏制作领域受到广泛关注。近年来，随着transformer（Transformer）和diffusion model（扩散模型）等新型模型的引入，该领域取得了重大突破。当前方法不仅能生成新内容，还可对生成素材进行编辑。本综述系统梳理了该技术，将其划分为三大核心领域：肖像生成、驱动机制和编辑技术。我们总结了各领域的里程碑研究，并批判性分析了其创新点与不足。此外，我们整理了大量数据集，并基于多种评估指标对现有方法的性能进行了全面分析，旨在为未来研究提供清晰的框架和可靠的数据支撑。最后，我们探讨了说话人头部合成的应用场景，结合具体案例进行说明，并研究了潜在的未来方向。
英文摘要：
Talking head synthesis, an advanced method for generating portrait videos from a still image driven by specific content, has garnered widespread attention in virtual reality, augmented reality and game production. Recently, significant breakthroughs have been made with the introduction of novel models such as the transformer and the diffusion model. Current methods can not only generate new content but also edit the generated material. This survey systematically reviews the technology, categorizing it into three pivotal domains: portrait generation, driven mechanisms, and editing techniques. We summarize milestone studies and critically analyze their innovations and shortcomings within each domain. Additionally, we organize an extensive collection of datasets and provide a thorough performance analysis of current methodologies based on various evaluation metrics, aiming to furnish a clear framework and robust data support for future research. Finally, we explore application scenarios of talking head synthesis, illustrate them with specific cases, and examine potential future directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
