Practical overview
When it fits
Use the repository when you need a research-code workflow for image animation and can work with Python, configuration files, checkpoints, and—particularly for training—the datasets and CUDA-based commands described in the README. For a quick trial, the README also links hosted demos.
Who it is for
- Developers exploring image animation or motion-transfer research.
- Researchers working on face reenactment, talking-head animation, or pose-driven image workflows.
- Python users who want to adapt or train the published implementation.
Documented uses
- Animate a still source image using a driving video.
- Explore motion-transfer and face-animation workflows using a pretrained checkpoint.
- Train the model on a supported dataset and inspect checkpoints, loss values, and reconstruction results.
- Evaluate video reconstruction and save generated video and PNG frames.
Requirements & limits
The project is publicly available as an MIT-licensed GitHub repository. The README links hosted demos on Hugging Face Spaces and Replicate, as well as a Google Colab notebook.
- Local demos require a configuration and checkpoint in addition to source-image and driving-video inputs.
- The repository recommends Python 3.9 and installs dependencies from requirements.txt.
- Training requires dataset preparation; the README gives separate dataset guidance for MGif, TaiChiHD, VoxCeleb, and TED-talks.
- Documented training and evaluation commands use CUDA device settings.
- The README links pre-trained checkpoints separately from the source code and identifies FOMM and MRAA as code foundations.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Run the image-animation demo from the repository
Generate an animation from a source image and a driving video using the documented Python demo.
Before you start
- Python 3; Python 3.9 is recommended.
- Install dependencies from requirements.txt.
- A configuration file, a checkpoint, a source image, and a driving video.
Steps
Install the project dependencies with pip install -r requirements.txt.
Choose a dataset configuration and obtain a compatible checkpoint from the linked pre-trained model sources.
Run demo.py with the configuration, checkpoint, source image, and driving video arguments shown in the README.
Expected outcome
The documented demo workflow uses the supplied image and driving video to create an image animation.
Guide limitations
- The README's example command sets CUDA_VISIBLE_DEVICES and uses the Vox configuration and checkpoint; it does not document a hardware-free execution route.
- The repository does not establish that a particular checkpoint, configuration, or input combination will work for every user's files.
Prepare the repository environment
Install the documented Python dependencies before using the repository's demos or training workflow.
Before you start
- Python 3, with Python 3.9 recommended.
- A local copy of the repository.
Steps
Use a Python 3 environment.
Install dependencies with pip install -r requirements.txt.
Review the YAML files in config/ and the parameter descriptions in config/taichi-256.yaml.
Expected outcome
The documented dependencies are installed and the available configuration structure is identified.
Guide limitations
- These setup instructions do not by themselves run the model or demonstrate that the environment is compatible with a particular machine.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- yoyo-nb/Thin-Plate-Spline-Motion-Model · Official homepage title · retained in its source language