Awesome-Talking-Head-Synthesis
- Datasets
- Survey
- Funny Work
- Audio-driven
- Text-driven
- NeRF & 3D & Gaussian Splatting
- Metrics
- Tools & Software
- Slides & Presentations
- References
- Star History
This repository organizes papers, codes and resources related to generative adversarial networks (GANs) 🤗 and neural radiance fields (NeRF) 🎨, with a main focus on image-driven and audio-driven talking head synthesis papers and released codes. 👤
Papers for Talking Head Synthesis, released codes collections. ✍️
Most papers are linked to PDFs on "arXiv" or journal/conference websites 📚. However, some papers require an academic license to view 🔐.
🔆 This project Awesome-Talking-Head-Synthesis is ongoing - pull requests are welcome! If you have any suggestions (missing papers, new papers, key researchers or typos), please feel free to edit and submit a PR. You can also open an issue or contact me directly via email. 📩
⭐ If you find this repo useful, please give it a star! 🤩
2023.12 Update 📆
Thank you to https://github.com/Curated-Awesome-Lists/awesome-ai-talking-heads, I have added some of its contents, such as Tools & Software and Slides & Presentations. 🙏 I hope this will be helpful.😊
If you have any feedback or ideas on extending this aggregated resource, please open an issue or PR - community contributions are vital to advancing this shared knowledge. 🤝
Let's keep pushing forward to recreate ever more realistic digital human faces! 💪 We've come so far but still have a long way to go. With continued research 🔬 and collaboration, I'm sure we'll get there! 🤗
Please feel free to star ⭐ and share this repo if you find it a valuable resource. Your support helps motivate me to keep maintaining and improving it. 🥰 Let me know if you have any other questions!
Datasets
| Dataset | Download Link | Description |
|---|---|---|
| Faceforensics++ | Download link | |
| CelebV | Download link | |
| VoxCeleb | Download link | VoxCeleb, a comprehensive audio-visual dataset for speaker recognition, encompasses both VoxCeleb1 and VoxCeleb2 datasets. |
| VoxCeleb1 | Download link | VoxCeleb1 contains over 100,000 utterances for 1,251 celebrities, extracted from videos uploaded to YouTube. |
| VoxCeleb2 | Download link | Extracted from YouTube videos, VoxCeleb2 includes video URLs and discourse timestamps. As the largest public audio-visual dataset, it is primarily used for speaker recognition tasks. However, it can also be utilized for training talking-head generation models. To obtain download permission and access the dataset, apply here. Requires 300 GB+ storage space. |
| ObamaSet | Download link | ObamaSet is a specialized audio-visual dataset focused on analyzing the visual speech of former US President Barack Obama. All video samples are collected from his weekly address footage. Unlike previous datasets, it exclusively centers on Barack Obama and does not provide any human annotations. |
| TalkingHead-1KH | Download link | The dataset consists of 500k video clips, of which about 80k are greater than 512x512 resolution. Only videos under permissive licenses are included. Note that the number of videos differ from that in the original paper because a more robust preprocessing script was used to split the videos. |
| LRW (Lip Reading in the Wild) | Download link | LRW, a diverse English-speaking video dataset from the BBC program, features over 1000 speakers with various speaking styles and head poses. Each video is 1.16 seconds long (29 frames) and involves the target word along with context. |
| MEAD 2020 | Download link | MEAD 2020 is a Talking Head dataset annotated with emotion labels and intensity labels. The dataset focuses on facial generation for natural emotional speech, covering eight different emotions on three intensity levels. |
| CelebV-HQ | Download link | CelebV-HQ is a high-quality video dataset comprising 35,666 clips with a resolution of at least 512x512. It includes 15,653 identities, and each clip is manually labeled with 83 facial attributes, spanning appearance, action, and emotion. The dataset's diversity and temporal coherence make it a valuable resource for tasks like unconditional video generation and video facial attribute editing. |
| HDTF | Download link | HDTF, the High-definition Talking-Face Dataset, is a large in-the-wild high-resolution audio-visual dataset consisting of approximately 362 different videos totaling 15.8 hours. Original video resolutions are 720 P or 1080 P, and each cropped video is resized to 512 × 512. |
| CREMA-D | Download link | CREMA-D is a diverse dataset with 7,442 original clips featuring 91 actors, including 48 male and 43 female actors aged 20 to 74, representing various races and ethnicities. The dataset includes recordings of actors speaking from a set of 12 sentences, expressing six different emotions (Anger, Disgust, Fear, Happy, Neutral, and Sad) at four emotion levels (Low, Medium, High, and Unspecified). Emotion and intensity ratings were gathered through crowd-sourcing, with 2,443 participants rating 90 unique clips each (30 audio, 30 visual, and 30 audio-visual). Over 95% of the clips have more than 7 ratings. For additional details on CREMA-D, refer to the paper link. |
| LRS2 | Download link | LRS2 is a lip reading dataset that includes videos recorded in diverse settings, suitable for studying lip reading and visual speech recognition. |
| GRID | Download link | The GRID dataset was recorded in a laboratory setting with 34 volunteers, each speaking 1000 phrases, totaling 34,000 utterance instances. Phrases follow specific rules, with six words randomly selected from six categories: "command," "color," "preposition," "letter," "number," and "adverb." Access the dataset here. |
| SAVEE | Download link | The SAVEE (Surrey Audio-Visual Expressed Emotion) database is a crucial component for developing an automatic emotion recognition system. It features recordings from 4 male actors expressing 7 different emotions, totaling 480 British English utterances. These sentences, selected from the standard TIMIT corpus, are phonetically balanced for each emotion. Recorded in a high-quality visual media lab, the data undergoes processing and labeling. Performance evaluation involves 10 subjects rating recordings under audio, visual, and audio-visual conditions. Classification systems for each modality achieve speaker-independent recognition rates of 61%, 65%, and 84% for audio, visual, and audio-visual, respectively. |
| BIWI(3D) | Download link | The Biwi 3D Audiovisual Corpus of Affective Communication serves as a compromise between data authenticity and quality, acquired at ETHZ in collaboration with SYNVO GmbH. |
| VOCA | Download link | VOCA is a 4D-face dataset with approximately 29 minutes of 4D face scans and synchronized audio from 12-bit speakers. It greatly facilitates research in 3D VSG. |
| Multiface(3D) | Download link | The Multiface Dataset consists of high-quality multi-view video recordings of 13 people displaying various facial expressions. It contains approximately 12,200 to 23,000 frames per subject, captured at 30 fps from around 40 to 160 camera views with uniform lighting. The dataset's size is 65TB and includes raw images (2048x1334 resolution), tracked and meshed heads, 1024x1024 unwrapped face textures, camera calibration metadata, and audio. This repository provides code for downloading the dataset and building a codec avatar using a deep appearance model. |
| MMFace4D | Download link | The MMFace4D dataset is a large-scale multi-modal dataset for audio-driven 3D facial animation research. It contains over 35,000 sequences captured from 431 subjects ranging in age from 15 to 68 years old. Various sentences from scenarios such as news broadcasting, conversations and storytelling were recorded, totaling around 11,000 utterances. High-fidelity data was captured using three synchronized RGB-D cameras to obtain high-resolution 3D meshes and textures. A reconstruction pipeline was developed to fuse the multi-view data and generate topology-consistent 3D mesh sequences. In addition to the 3D facial motions, synchronized speech audio is also provided. The final dataset covers a wide range of expressive talking styles and facial expressions through a diverse set of subjects and utterances. With its large scale, high quality of data and strong diversity, the MMFace4D dataset provides an ideal benchmark for developing and evaluating audio-driven 3D facial animation models. |
Survey
Funny Work
| Year | Title | Code | Project | Keywords |
|---|---|---|---|---|
| 2024 | [Audio2Photoreal] From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations | Code | Project | Photoreal |
| 2024 | [Animate Anyone] Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation | Code | Project | 🔥Animate (阿里科目三驱动) |
| 2024 | [3DGAN] What You See Is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs | Project | 🔥Nvidia | |
| 2024 | [LivePortrait] LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control | Codea | Project | 🔥快手 |
Audio-driven
| Year | Title | Conference/Journal | Code | Project | Keywords |
|---|---|---|---|---|---|
| 2024 | [GLDiTalker] GLDiTalker: Speech-Driven 3D Facial Animation with Graph Latent Diffusion Transformer | Arxiv 2024 | |||
| 2024 | Landmark-guided Diffusion Model for High-fidelity and Temporally Coherent Talking Head Generation | Arxiv 2024 | |||
| 2024 | [JambaTalk] JambaTalk: Speech-Driven 3D Talking Head Generation Based on Hybrid Transformer-Mamba Model | Arxiv 2024 | 3D | ||
| 2024 | [Talk Less, Interact Better] Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal LLMs | COLM 2024 | Code | LLM | |
| 2024 | [Digital Avatars] Digital Avatars: Framework Development and Their Evaluation | Arxiv 2024 | |||
| 2024 | [EmoTalk3D] EmoTalk3D: High-Fidelity Free-View Synthesis of Emotional 3D Talking Head | ECCV 2024 | Project | ||
| 2024 |
编辑推荐精选
讯飞智文
一键生成PPT和Word，让学习生活更轻松
讯飞智文是一个利用 AI 技术的项目，能够帮助用户生成 PPT 以及各类文档。无论是商业领域的市场分析报告、年度目标制定，还是学生群体的职业生涯规划、实习避坑指南，亦或是活动策划、旅游攻略等内容，它都能提供支持，帮助用户精准表达，轻松呈现各种信息。
讯飞星火
深度推理能力全新升级，全面对标OpenAI o1
科大讯飞的星火大模型，支持语言理解、知识问答和文本创作等多功能，适用于多种文件和业务场景，提升办公和日常生活的效率。讯飞星火是一个提供丰富智能服务的平台，涵盖科技资讯、图像创作、写作辅助、编程解答、科研文献解读等功能，能为不同需求的用户提供便捷高效的帮助，助力用户轻松获取信息、解决问题，满足多样化使用场景。
Spark-TTS
一种基于大语言模型的高效单流解耦语音令牌文本到语音合成模型
Spark-TTS 是一个基于 PyTorch 的开源文本到语音合成项目，由多个知名机构联合参与。该项目提供了高效的 LLM（大语言模型）驱动的语音合成方案，支持语音克隆和语音创建功能，可通过命令行界面（CLI）和 Web UI 两种方式使用。用户可以根据需求调整语音的性别、音高、速度等参数，生成高质量的语音。该项目适用于多种场景，如有声读物制作、智能语音助手开发等。
Trae
字节跳动发布的AI编程神器IDE
Trae是一种自适应的集成开发环境（IDE），通过自动化和多元协作改变开发流程。利用Trae，团队能够更快速、精确地编写和部署代码，从而提高编程效率和项目交付速度。Trae具备上下文感知和代码 自动完成功能，是提升开发效率的理想工具。
咔片PPT
AI助力，做PPT更简单！
咔片是一款轻量化在线演示设计工具，借助 AI 技术，实现从内容生成到智能设计的一站式 PPT 制作服 务。支持多种文档格式导入生成 PPT，提供海量模板、智能美化、素材替换等功能，适用于销售、教师、学生等各类人群，能高效制作出高品质 PPT，满足不同场景演示需求。
讯飞绘文
选题、配图、成文，一站式创作，让内容运营更高效
讯飞绘文，一个AI集成平台，支持写作、选题、配图、排版和发布。高效生成适用于各类媒体的定制内容，加速品牌传播，提升内容营销效果。
材料星
专业的AI公文写作平台，公文写作神器
AI 材料星，专业的 AI 公文写作辅助平台，为体制内工作人员提供高效的公文写作解决方案。拥有海量公文文库、9 大核心 AI 功能，支持 30 + 文稿类型生成，助力快速完成领导讲话、工作总结、述职报告等材料，提升办公效率，是体制打工人的得力写作神器。
openai-agents-python
OpenAI Agents SDK，助力开发者便捷使用 OpenAI 相关功能。
openai-agents-python 是 OpenAI 推出的一款强大 Python SDK，它为开发者提供了与 OpenAI 模型交互的高效工具，支持工具调用、结果处理、追踪等功能，涵盖多种应用场景，如研究助手、财务研究等，能显著提升开发效率，让开发者更轻松地利用 OpenAI 的技术优势。
Hunyuan3D-2
高分辨率纹理 3D 资产生成
Hunyuan3D-2 是腾讯开发的用于 3D 资产生成的强大工具，支持从文本描述、单张图片或多视角图片生成 3D 模型，具备快速形状生成能力，可生成带纹理的高质量 3D 模型，适用于多个领域，为 3D 创作提供了高效解决方案。
3FS
一个具备存储、管理和客户端操作等多种功能的分布式文件系统 相关项目。
3FS 是一个功能强大的分布式文件系统项目，涵盖了存储引擎、元数据管理、客户端工具等多个模块。它支持多种文件操作，如创建文件和目录、设置布局等，同时具备高效的事件循环、节点选择和协程池管理等特性。适用于需要大规模数据存储和管理的场景，能够提高系统的性能和可靠性，是分布式存储领域的优质解决方案。
推荐工具精选
AI云服务特惠
懂AI专属折扣关注微信公众号
最新AI工具、AI资讯
独家AI资源、AI项目落地
微信扫一扫关注公众号