🧠 Talking Heads, but Make It Science

January 28, 2026 · View on GitHub

A Funny Yet Serious Survey on Deepfake’s Nerdy Cousin: Talking Head Generation

“Ever wished your selfie could talk during Zoom calls? We’re not quite there... but we’ve already made it nod in agreement and blink suspiciously."
— @mazumdarsoumya

📘 What’s Going On Here?

Welcome to the wildest ride in neural rendering: Talking Head Generation (THG). This repo is your cheat code to understanding a field that blends deep learning with deep confusion, peppered with a little madness and a lot of metrics.

⚠️ DISCLAIMER:
The preprint paper titled
📄 “Advancing Talking Head Generation: A Comprehensive Survey of Multi-Modal Methodologies, Datasets, Evaluation Metrics, and Loss Functions” is NOT going to be published. It’s the mother of all drafts — split into multiple, more detailed children destined for Scopus-indexed journals and conferences. arXiv:2507.02900

📚 The Paper Children (Coming Soon™️, but Real)

These aren’t clones. They are deeper, sharper, peer-reviewed cousins of the main survey. Once they pass reviewer boss fights, we’ll drop the DOIs and links here:

🔍 1. Advancements in talking head generation: a comprehensive review of techniques, metrics, and challenges

🧪 Journal - The Visual Computer, Springer Nature DOI: 10.1007/s00371-025-04232-w

Dive into the model jungle — from GANs to NeRFs and Transformers, all neatly dissected like a biology lab frog.

📃️ 2. Comprehensive Dataset Analysis for Talking Head Generation

📘 Book Chapter - Centering Transparency and Trust in Data and AI Ecosystems, IGI Global

What’s inside VoxCeleb? Why is GRID so... griddy? This one's for dataset diggers.

📏 3. Quantitative Assessment in Talking Head Generation: Metrics and Loss Functions

📗 Journal Submission

If you've ever said "SSIM is enough," this chapter is about to throw shade and math at you.

🧪 4. Empirical Evaluation of State-of-the-Art Talking Head Generation Models

🛠️ Conference Paper - 3rd International Conference on Recent Advances in Artificial Intelligence and Smart Applications

Benchmarks, metrics, results, regrets — actual experimental results with charts and enough tables to furnish an IKEA showroom.

🔬 Links will appear here like magic scrolls, post-acceptance. Until then... stay tuned.

🧪 What's in This Repo?

A research buffet for your GPU and gray matter:

🧠 500+ Research Papers distilled into human language
🧑‍🏫 Categorization across modalities: Audio, Video, Image, Text, 2D/3D, GAN, NeRF, Transformer-based, and more
🎞️ Datasets Decoded: VoxCeleb, GRID, LRS3, CelebV, and other acronyms we pretend to remember
🔬 Evaluation Metrics Galore: SSIM, PSNR, CPBD, LPIPS, LMD, WER, CSIM — enough for a PhD defense
💥 Loss Functions Explained: From Mean Squared Error to “Oh no, my perceptual loss exploded”
🧪 Code + Sample Outputs — because seeing is believing, and benchmarks don’t screenshot themselves

🎮 Sample Outputs

Model	Output
Wav2Lip	🎥 Watch
Wav2Lip (Generated)	🎥 Watch
SadTalker	🎥 Watch
SadTalker (Generated)	🎥 Watch

Not DeepFakes. Just DeepWork.

⚙️ Code Zone

Coming soon in eval-scripts/:

compute_metrics.py – For SSIM, PSNR, CPBD, LMD, and that one metric your professor insists on using
align_faces.py – Because misaligned faces are worse than misaligned deadlines
visualize_lipsync.py – For pixel-by-pixel judgement of your model's karaoke skills

Wanna try it? Just:

git clone --depth 1 --force https://github.com/VineetKumarRakesh.git

(Replace VineetKumarRakesh when you’re not lazy.)

📊 Benchmarks Snapshot

Model	Dataset	SSIM ↑	PSNR ↑	LMD ↓
Wav2Lip	VoxCeleb2	0.74	32.5	1.21
FOMM	VoxCeleb1	0.68	29.8	1.49
Face-vid2vid	GRID	0.72	31.2	1.33

↑ Good. ↓ Also good. ∞? You messed up somewhere.

🧠 Core Contributions (aka "Too Long, Just Tell Me Why It Matters")

📀 Unified taxonomy for THG — no more buzzword soup
🔬 Benchmarked open-source models — because someone had to do the hard part
📦 Dataset comparison — what’s hot, what’s not
📏 Metrics mayhem — why SSIM isn’t always your friend
🎼 Loss functions and why they love making your training unstable

🔖 Citation (For When It’s Published. Soon.)

@misc{rakesh2025talkingheadreview,
  title={Advancing Talking Head Generation: A Comprehensive Survey of Multi-Modal Methodologies, Datasets, Evaluation Metrics, and Loss Functions},
  author={Vineet Kumar Rakesh and Soumya Mazumdar and Research Pratim Maity and Sarbajit Pal and Amitabha Das and Tapas Samanta},
  year={2025},
  note={Preprint – Will not be published. Child papers incoming.}
}

📣 Final Words

If you're into:

Machines that talk with your face
Academic deep dives that make your brain sweat
And humor that makes research tolerable

You're in the right repo.

Stars appreciated. Forks encouraged. Pull requests cautiously welcomed.
🥸 Your talking head just said thanks.