JingyunLiang/VRT
VRT: A Video Restoration Transformer (official repository) observed · 2026-08-28
Health v2 · maintenance only
23/100
- Activity 0
- Release rhythm 8
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1688
- days_rel: n/a
- days_push: 1172
- n_releases_24m: 0
Adoption not part of the score
1546 stars · 141 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
VRT is the official PyTorch implementation of the paper 'VRT: A Video Restoration Transformer', a transformer-based model for video restoration tasks. It provides pretrained models and training/evaluation code for video super-resolution, deblurring, denoising, and frame interpolation.
Use cases
- restore low-quality video frames to high quality
- upscale low-resolution videos with super-resolution
- remove blur from shaky or motion-blurred videos
- denoise noisy video footage
- interpolate missing frames in a video sequence
- reproduce state-of-the-art video restoration benchmarks like REDS and Vimeo90K
When to choose
- you need a transformer-based model for video super-resolution, deblurring, or denoising
- you want pretrained checkpoints to run video restoration experiments quickly
- you are doing research on low-level vision and need a strong baseline
When to avoid
- you need real-time video processing on consumer hardware
- you want a production-ready application with a GUI rather than research code
- you need a permissively licensed library since the license is non-standard
Facets
library · maturity maintenance
machine-learning deep-learning image-processing video-processing computer-vision deep-learning machine-learning python vision-transformer video-super-resolution video-deblurring video-denoising frame-interpolation pytorch research-code pretrained-models video gpu
6 sources
- readme: https://github.com/JingyunLiang/VRT · fetched 2026-08-28 · a798db323f7a
- homepage: https://arxiv.org/abs/2201.12288 · fetched 2026-08-29 · 372b663884fc
- site_page: https://info.arxiv.org/about/donate.html · fetched 2026-08-29 · cca9c3a11c56
- site_page: https://info.arxiv.org/about/ourmembers.html · fetched 2026-08-29 · 47cbc55ff1de
- site_page: https://info.arxiv.org/about · fetched 2026-08-29 · a1f16f915a9a
- site_page: https://info.arxiv.org/labs/index.html · fetched 2026-08-29 · b14a8d05a0ec
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| JingyunLiang/VRT | main | 23 |
For agents
markdown · JSON · MCP: product_card(name="JingyunLiang/VRT")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem