SparkVSR：通过稀疏关键帧传播实现交互式视频超分辨率
SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation
AI总结：
SparkVSR提出一种交互式视频超分辨率框架，通过稀疏关键帧作为控制信号，提升时间一致性和恢复质量，实验显示在多个基准上优于基线方法。
摘要：
视频超分辨率（VSR）旨在从低分辨率（LR）估计中恢复高质量视频帧，但现有方法在推理时像黑箱：用户无法可靠纠正意外伪影，只能接受模型输出。本文提出新颖的交互式VSR框架SparkVSR，使稀疏关键帧成为简单且表达性强的控制信号。具体而言，用户首先使用任何现成的图像超分辨率（ISR）模型超分辨率或可选的小组关键帧，然后SparkVSR将关键帧先验传播到整个视频序列，同时基于原始LR视频运动保持稳定。具体来说，我们引入了关键帧条件的潜在像素两阶段训练流程，将LR视频潜在与稀疏编码的HR关键帧潜在融合，以学习稳健的跨空间传播并细化感知细节。在推理时，SparkVSR支持灵活的关键帧选择（手动指定、编码器I帧提取或随机采样）和无参考引导机制，持续平衡关键帧遵循与盲恢复，即使参考关键帧缺失或不完美也能保持鲁棒性能。在多个VSR基准上的实验显示，时间一致性和恢复质量得到提升，优于基线方法达24.6%、21.8%和5.6%（CLIP-IQA、DOVER、MUSIQ）。此外，我们证明SparkVSR是一种通用的交互式、关键帧条件的视频处理框架，可直接应用于未见过的任务如老电影修复和视频风格迁移。项目页面可访问：https://sparkvsr.github.io/
英文摘要：
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. Our project page is available at: https://sparkvsr.github.io/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
