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ARXIV:2603.16864 · VIDEO SUPER-RESOLUTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.16864VIDEO SUPER-RESOLUTIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEJiongze Yu · Xiangbo Gao · Pooja Verlani · Akshay Gadde · Yilin Wang · Balu Adsumilli · +1 at arXiv
SparkVSR offers an interactive framework for video super-resolution that allows users to control output quality through keyframe selection.
Opportunity summary
Pain SparkVSR offers an interactive framework for video super-resolution that allows users to control output quality through keyframe selection.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
SparkVSR offers an interactive framework for video super-resolution that allows users to control output quality through keyframe selection. In this paper, we propose a novel interactive VSR framework dubbed SparkVSR that makes sparse keyframes…
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…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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…
Video Super-Resolution moved forward this cycle; last verified April 2026. Public score 8.0/10.
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SparkVSR offers an interactive framework for video super-resolution that allows users to control output quality through keyframe selection.
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10.48550/arXiv.2603.16864SparkVSR offers an interactive framework for video super-resolution that allows users to control output quality through keyframe selection.
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. Our project page is available at: https://sparkvsr.github.io/
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Dimensions overall score 8.0
PROBLEM
SparkVSR offers an interactive framework for video super-resolution that allows users to control output quality through keyframe selection. In this paper, we propose a novel interactive VSR framework dubbed SparkVSR that makes sparse keyframes a simple and expressive control sig...
METHOD
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 mod...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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 key...
WHY NOW
Video Super-Resolution moved forward this cycle; last verified April 2026. Public score 8.0/10.
Abstract-backed public claims while anchored extraction refreshes.
SparkVSR offers an interactive framework for video super-resolution that allows users to control output quality through keyframe selection. In this paper, we propose a novel interactive VSR framework dubbed SparkVSR that makes sparse keyframes a simple and expressive control signal.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Video Super-Resolution moved forward this cycle; last verified April 2026. Public score 8.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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SparkVSR offers an interactive framework for video super-resolution that allows users to control output quality through keyframe selection.
Segment
Video Super-Resolution
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