Practical overview
When it fits
Use STB-VMM when small movement in video is difficult to observe and you can provide the required local dependencies and a pretrained checkpoint, or when you want to train the model using a dataset.
Who it is for
- Researchers and developers working with video where small movements need to be made more visible.
- Practitioners exploring motion magnification in biomedical footage, structural analysis or predictive maintenance.
- Python and PyTorch users who can work with a local model repository, dependencies and checkpoints.
Documented uses
- Amplify subtle movement in an input video for visual inspection.
- Train the model using a training dataset and configured parameters.
- Prepare magnified video sequences for downstream inspection or measurement.
Requirements & limits
The repository is public and lists an MIT license. The README describes Linux-style shell commands and provides an alternative PowerShell script for Windows.
- Inference requires a pretrained checkpoint and FFmpeg.
- Training is handled separately through train.py and requires a training dataset.
- The project is a code-and-model implementation, so users need to run the workflow in their own environment.
- The README notes that distinguishing very subtle movement from noise is challenging.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Magnify movement in a video with the inference script
Create a motion-magnified video using the repository's provided script.
Before you start
- Install the dependencies with pip install -r requirements.txt.
- Install FFmpeg, which the README identifies as required for magnify_video.
- Have an input video and a pretrained checkpoint.
Steps
Run magnify_video.sh with arguments for magnification, input video, checkpoint, output name, source directory and frame rate, following the README example.
On Windows, use the alternative PowerShell script provided by the repository.
Expected outcome
A video processed to amplify small movements.
Guide limitations
- A pretrained checkpoint is required for magnification.
- The example command's paths and argument values are examples, not a guarantee of suitability for every video.
Set up model training with the provided script
Prepare a training run using the repository's train.py script.
Before you start
- Install the dependencies with pip install -r requirements.txt.
- Obtain a training dataset; the README links to a dataset download.
Steps
Set the training data path and choose values for the dataset size, workers, batch size, learning rate and epochs.
Run train.py with the chosen arguments, following the README's example.
Expected outcome
A training run started with the repository's script and supplied dataset.
Guide limitations
- The README does not provide a complete account of dataset preparation or training results.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- RLado/STB-VMM · Official homepage title · retained in its source language