Moore-AnimateAnyone Project Overview
Introduction
The Moore-AnimateAnyone project is a cutting-edge technology designed to breathe life into static images by generating animations based on a separate driving video. Its core functionality focuses on face reenactment, where the facial landmarks from a driving video control the pose of a source image, preserving the identity of the original face while accurately mimicking its expressions and movements. Such advancements can precisely replicate both head and mouth motions, including subtle features like eye blinks.
Latest Updates
Moore-AnimateAnyone has released not only the inference codes and pretrained models for face reenactment but also training scripts, allowing users to train their own models. The project has launched an interactive demo on HuggingFace Spaces, providing a hands-on experience with the technology.
Project Details
The project is built upon the AnimateAnyone pipeline, originally developed by the HumanAIGC community. Moore-AnimateAnyone seeks to replicate approximately 80% of the results demonstrated in its inspiration while continuously improving for higher accuracy and realism.
Current Progress and Releases
The development team has successfully released:
- Inference codes and pretrained models for AnimateAnyone.
- Training scripts for creating custom AnimateAnyone models.
- Inference tools for face reenactment.
However, some elements, such as training scripts for face reenactment and audio-driven portrait video generation, remain to be released, indicating ongoing work to enhance the project's features and capabilities.
Demonstrations
AnimateAnyone: Demonstrated at a resolution of 512x768, these examples showcase the potential of technology to bring static images to life with minimal artifacts. However, some limitations like background artifacts and possible flickering under certain conditions are noted.
Face Reenactment: Results here are also promising, producing engaging animations from static images at a resolution of 512x512.
Installation Instructions
To set up Moore-AnimateAnyone, a Python environment of version 3.10 or higher, with cuda version 11.7, is recommended. The installation process involves setting up the environment using pip and other dependencies necessary for face landmark extraction.
Training and Inference Process
Both AnimateAnyone inference and face reenactment inference scripts can be executed via provided command-line tools. Users can also train their models after preparing the necessary data and weights. The comprehensive training process includes a two-stage approach, starting from initializing with pretrained weights to training the motion module.
Gradio and Community Contributions
A Gradio demo, supported by HuggingFace, allows users to experience the technology immediately, though scaled down to manage resource consumption. Users with available GPU resources can run a local version for a more extensive demonstration.
Future Integrations
Moore-AnimateAnyone is also aiming to launch on the MoBi MaLiang AIGC platform, allowing broader access via cloud computing solutions.
Disclaimer
Moore-AnimateAnyone is dedicated to academic research, emphasizing a disclaimer on the responsible use of its generative models. The creators do not claim responsibility for how the technology is used, urging users to adhere to ethical and legal standards.
Acknowledgements
Finally, the project extends thanks to various contributors and open-source communities, whose research and shared resources have been foundational in developing and enhancing Moore-AnimateAnyone.
Through its innovations, Moore-AnimateAnyone is paving the way for advanced animation technologies that seamlessly merge reality and digital creativity, opening new horizons in digital art and media production.