What it does STTN fills missing regions in videos—think removing a pedestrian who walks through your shot—by attending to patches across both space and time simultaneously. It uses multi-scale patch-based attention modules and a spatial-temporal adversarial loss, trained on standard datasets like YouTube-VOS and DAVIS.

The interesting bit Instead of the usual frame-by-frame or purely spatial approaches, STTN processes all input frames at once with joint spatial-temporal transformers. The attention visualization notebook suggests you can actually inspect where the model is “looking” across the video to borrow pixels for the hole.

Key highlights

• Pretrained model available via Google Drive; one-line inference with test.py
• Supports both stationary masks and moving-object masks (the harder, realistic case)
• Includes TensorBoard training monitoring and a Jupyter notebook for attention visualization
• ECCV 2020 paper with slides and project page still live
• Conda environment file provided for reproducible setup

Caveats

• The README is sparse on architecture details; you’ll need the paper for the full method
• No explicit performance numbers or comparison tables in the repo itself
• Inference examples are limited to a single schoolgirls demo video

Verdict Worth a look if you’re doing video restoration, object removal, or need a baseline transformer for spatiotemporal tasks. Skip if you need a polished production tool—this is research code with the usual rough edges.