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first-order-model

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Project Description

This repository contains the source code for the paper First Order Motion Model for Image Animation

first-order-model: This repository contains the source code for the paper First Order Motion Model for Image Animation

Project Title

first-order-model — Advanced Image Animation with First Order Motion Model

Overview

The first-order-model project is an open-source repository that provides the source code for the paper "First Order Motion Model for Image Animation." It enables the animation of images by transferring motion from a driving video to a source image, creating realistic animated sequences. This project stands out for its ability to handle complex motions and its application in various datasets, showcasing its versatility in image animation.

Key Features

  • Realistic image animation through motion transfer
  • Support for multiple datasets (VoxCeleb, Fashion, MGIF)
  • Pre-trained model checkpoints for quick setup and use
  • Docker support for easy environment management

Use Cases

  • Content creators can use this model to animate static images for videos or presentations.
  • Researchers in computer vision can leverage the model for motion analysis and image generation studies.
  • Game developers can implement realistic character animations using motion transfer techniques.

Advantages

  • High-quality animation results with a simple setup process
  • Flexible configuration options to adapt to different datasets and requirements
  • Active community and repository updates, ensuring ongoing improvements and support

Limitations / Considerations

  • The project requires a good understanding of Python and Jupyter Notebooks for effective use.
  • Motion transfer quality may vary depending on the complexity of the source and driving videos.
  • The need for pre-trained checkpoints and compatible libraries might pose challenges for some users.

Similar / Related Projects

  • DeepArt: A project that uses deep learning to transform images into artistic styles, differing in its focus on style transfer rather than motion.
  • FaceSwap: A tool for swapping faces in images and videos, which, while related to image manipulation, focuses on facial replacement rather than motion transfer.
  • StyleGAN: A project that generates high-quality synthetic images, differing in its goal of image synthesis rather than animation.

Basic Information


📊 Project Information

🏷️ Project Topics

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🎮 Online Demos

📚 Documentation


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Project Information

Created on 12/11/2019
Updated on 10/3/2025