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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[ECCV 2026] SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation
| Date | Stars |
|---|---|
| 2026-07-24 | 692 |
| 2026-07-25 | 692 |
| 2026-07-28 | 692 |
| 2026-07-30 | 692 |
| 2026-08-06 | 692 |
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<div align="center">
<p><img src="assets/logo2.png" width="360px"></p>
<h1>SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation</h1>
<p>
Jiongze Yu<sup>1</sup>, Xiangbo Gao<sup>1</sup>, Pooja Verlani<sup>2</sup>, Akshay Gadde<sup>2</sup>,
Yilin Wang<sup>2</sup>, Balu Adsumilli<sup>2</sup>, Zhengzhong Tu<sup>†,1</sup>
</p>
<p>
<sup>1</sup>Texas A&M University <sup>2</sup>YouTube, Google
<br>
<sup>†</sup>Corresponding author
</p>
<h3><em>Accepted to ECCV 2026</em></h3>
<p>
<a href="https://sparkvsr.github.io/"><img src="https://img.shields.io/badge/Project-Page-Green"></a>
<a href="https://huggingface.co/JiongzeYu/SparkVSR"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue"></a>
<a href="https://arxiv.org/abs/2603.16864"><img src="https://img.shields.io/badge/arXiv-2603.16864-b31b1b.svg"></a>
</p>
</div>
> 💡 **Your ⭐ star means a lot to us and helps support the continuous development of this project!**
#### 📰 News
- **2026.06.20:** SparkVSR is now available on [RunningHub.ai](https://www.runninghub.ai/) and [CNAPS.ai](https://cnaps.ai/) via community deployments!
- **2026.06.18:** SparkVSR is accepted to ECCV 2026! 🎉🎉🎉
- **2026.05.11:** ComfyUI-SparkVSR is released.🚀🚀🚀
- **2026.03.17:** This repo is released.🔥🔥🔥
---
### Demo
<p align="center">
<a href="assets/demo.mp4">
<img src="assets/demo.gif" alt="SparkVSR demo">
</a>
</p>
---
> **Abstract:** Video Super-Resolution (VSR) aims to restore high-quality video frames from low-resolution (LR) estimates, yet most existing VSR approaches behave like black boxes at inference time: users cannot reliably correct unexpected artifacts, but instead can only accept whatever the model produces.
In this paper, we propose a novel interactive VSR framework dubbed SparkVSR that makes sparse keyframes a simple and expressive control signal. Specifically, users can first super-resolve or optionally a small set of keyframes using any off-the-shelf image super-resolution (ISR) model, then SparkVSR propagates the keyframe priors to the entire video sequence while remaining grounded by the original LR video motion.
Concretely, we introduce a keyframe-conditioned latent-pixel two-stage training pipeline that fuses LR video latents with sparsely encoded HR keyframe latents to learn robust cross-space propagation and refine perceptual details. At inference time, SparkVSR supports flexible keyframe selection (manual specification, codec I-frame extraction, or random sampling) and a reference-free guidance mechanism that continuously balances keyframe adherence and blind restoration, ensuring robust performance even when reference keyframes are absent or imperfect. Experiments on multiple VSR benchmarks demonstrate improved temporal consistency and strong restoration quality, surpassing baselines by up to 24.6\%, 21.8\%, and 5.6\% on CLIP-IQA, DOVER, and MUSIQ, respectively, enabling controllable, keyframe-driven video super-resolution.
Moreover, we demonstrate that SparkVSR is a generic interactive, keyframe-conditioned video processing framework as it can be applied out of the box to unseen tasks such as old-film restoration and video style transfer.
---
### Inference Pipeline
<p align="center">
<img src="assets/inference_pipeline.png">
</p>
---
### Training Pipeline
<p align="center">
<img src="assets/training_pipeline.png">
</p>
## 🔖 TODO
- ✅ Release inference code.
- ✅ Release pre-trained models.
- ✅ Release training code.
- ✅ Release project page.
- ✅ Release ComfyUI.
## ⚙️ Dependencies
- Python 3.10+
- PyTorch >= 2.5.0
- Diffusers
- Other dependencies (see `requirements.txt`)
```bash
# Clone the github repo and go to the directory
git clone https://github.com/taco-group/SparkVSR
cd SparkVSR
# Create and activate conda environment
conda create -n sparkvsr python=3.10
conda activate sparkvsr
# Install all required dependenExcerpt of 17,399 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ab6490c3a6bb15ba, topic:video-generation, topic:video-editing
matched fp:ab6490c3a6bb15ba, topic:llm
matched fp:ab6490c3a6bb15ba, topic:super-resolution, desc:super resolution, readme:super resolution
matched fp:ab6490c3a6bb15ba, topic:vlm