This repository serves as a curated collection of research papers and associated code for generating talking face videos, primarily driven by audio input. It targets researchers and developers in computer vision and graphics interested in state-of-the-art techniques for audio-visual synthesis, facial animation, and digital human creation. The benefit lies in providing a centralized, up-to-date resource for exploring and implementing cutting-edge methods in this rapidly evolving field.

How It Works

The collection covers a wide array of approaches, including diffusion models, NeRF-based methods, implicit representations, and transformer architectures. These techniques leverage discrete motion priors, disentangled facial attributes, and multi-modal fusion to achieve high-fidelity lip-sync, controllable expressions, and identity preservation. The advantage of this diverse compilation is the exposure to various architectural choices and their effectiveness in different aspects of talking face generation, from realism to expressiveness and generalization.

Quick Start & Requirements

This is a curated list of papers and code, not a runnable software package. To use any of the listed code, users must refer to the individual project repositories linked within the table. Requirements will vary significantly per project, often including Python, deep learning frameworks (PyTorch, TensorFlow), specific CUDA versions, and large datasets (e.g., LRS2, HDTF, MEAD).

Highlighted Details

• Comprehensive coverage of CVPR, ICCV, ICLR, and other top-tier conferences from 2019-2023.
• Includes links to code, papers, and datasets for over 50 distinct research projects.
• Categorizes methods by keywords such as "Diffusion," "NeRF," "Emotion," and "3D."
• Lists common evaluation metrics used in the field, including PSNR, SSIM, FID, and LSE.

Maintenance & Community

This repository appears to be a personal curation project. There are no explicit mentions of maintainers, community channels (like Discord/Slack), or a roadmap. The content is updated with recent publications, indicating ongoing curation.

Licensing & Compatibility

The repository itself does not have a specified license. Each linked code project will have its own license, which must be consulted for usage, distribution, and commercial compatibility. Many research codebases are released under permissive licenses (MIT, Apache 2.0) but may have restrictions on dataset usage or model weights.

Limitations & Caveats

This is a meta-repository and does not provide a unified interface or installation. Users must individually clone, set up, and run each project's code, which can be complex and resource-intensive. Some linked code repositories may be incomplete, outdated, or no longer actively maintained.