推进说话头生成：多模态方法、数据集、评估指标和损失函数的全面综述
Advancing Talking Head Generation: A Comprehensive Survey of Multi-Modal Methodologies, Datasets, Evaluation Metrics, and Loss Functions
AI总结：
本文全面综述了说话头生成的多模态方法、数据集、评估指标和损失函数，探讨了技术挑战与未来发展方向。
摘要：
说话头生成（THG）已成为计算机视觉领域的一项变革性技术，能够实现与图像、音频、文本或视频输入同步的逼真人类面孔合成。本文对说话头生成的方法和框架进行了全面回顾，将方法分为基于2D的、基于3D的、基于神经辐射场（NeRF）的、基于扩散的、参数驱动的等多种技术。它评估了算法、数据集和评估指标，同时强调了在数字虚拟形象、视频配音、超低比特率视频会议和在线教育等应用中至关重要的感知真实性和技术效率。该研究指出了依赖预训练模型、极端姿态处理、多语言合成和时间一致性等挑战。未来方向包括模块化架构、多语言数据集、混合模型（结合预训练和任务特定层）以及创新的损失函数。通过综合现有研究并探索新兴趋势，本文旨在为说话头生成领域研究人员和实践者提供可操作的见解。如需完整综述、代码和精选资源列表，请访问我们的GitHub仓库：https://github.com/VineetKumarRakesh/thg。
英文摘要：
Talking Head Generation (THG) has emerged as a transformative technology in computer vision, enabling the synthesis of realistic human faces synchronized with image, audio, text, or video inputs. This paper provides a comprehensive review of methodologies and frameworks for talking head generation, categorizing approaches into 2D--based, 3D--based, Neural Radiance Fields (NeRF)--based, diffusion--based, parameter-driven techniques and many other techniques. It evaluates algorithms, datasets, and evaluation metrics while highlighting advancements in perceptual realism and technical efficiency critical for applications such as digital avatars, video dubbing, ultra-low bitrate video conferencing, and online education. The study identifies challenges such as reliance on pre--trained models, extreme pose handling, multilingual synthesis, and temporal consistency. Future directions include modular architectures, multilingual datasets, hybrid models blending pre--trained and task-specific layers, and innovative loss functions. By synthesizing existing research and exploring emerging trends, this paper aims to provide actionable insights for researchers and practitioners in the field of talking head generation. For the complete survey, code, and curated resource list, visit our GitHub repository: https://github.com/VineetKumarRakesh/thg.
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。
