Talking Head Anime 2 Demo

Demo programs for the Talking Head Anime from a Single Image 2: More Expressive project.

AI & Machine LearningPythonMIT

Abstract

Talking Head Anime 2 Demo is an open-source AI & Machine Learning project. Demo programs for the Talking Head Anime from a Single Image 2: More Expressive project. a single image, through a graphical user interface. The poser is available in two forms: a standard GUI application, and a Jupyter notebook. It is built using Python, Computer Vision, Deep Learning, Machine Learning, PyTorch. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

a single image, through a graphical user interface. The poser is available in two forms: a standard GUI application, and a Jupyter notebook.

called iFacialMocap, to an image of an anime character.

Both programs require a recent and powerful Nvidia GPU to run. I could personally ran them at good speed with the Nvidia Titan RTX. However, I think recent high-end gaming GPUs such as the RTX 2080, the RTX 3080, or better would do just as well.

2. Objective

Demo programs for the Talking Head Anime from a Single Image 2: More Expressive project.

This project demonstrates how Python, Computer Vision, Deep Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

PythonComputer VisionDeep LearningMachine LearningPyTorch

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/pkhungurn/talking-head-anime-2-demo.git
cd talking-head-anime-2-demo

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by pkhungurn and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

Want to build this as your internship project?

Work on an AI & Machine Learning project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.

Apply for AI & Machine Learning Internship