Stream-DiffVSR enables practical low-latency video super-resolution by combining a four-step distilled denoiser, auto-regressive temporal guidance, and a temporal processor in a strictly causal pipeline.
Temporal-consistent video restoration with pre-trained diffusion models
3 Pith papers cite this work. Polarity classification is still indexing.
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FMPlug adapts foundation flow-matching models into practical priors for inverse problems by combining instance-guided warm-start with sharp Gaussianity regularization, showing superior results on image restoration and scientific tasks with limited samples.
TIGER is a tri-prior fusion method for face video restoration using identity, geometry, and generative priors with progressive training to achieve SOTA identity fidelity and temporal stability on a new large-scale dataset.
citing papers explorer
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Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion
Stream-DiffVSR enables practical low-latency video super-resolution by combining a four-step distilled denoiser, auto-regressive temporal guidance, and a temporal processor in a strictly causal pipeline.
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Saving Foundation Flow-Matching Priors for Inverse Problems
FMPlug adapts foundation flow-matching models into practical priors for inverse problems by combining instance-guided warm-start with sharp Gaussianity regularization, showing superior results on image restoration and scientific tasks with limited samples.
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TIGER: Taming Identity, Geometry, and Generative Priors for High-Quality Face Video Restoration
TIGER is a tri-prior fusion method for face video restoration using identity, geometry, and generative priors with progressive training to achieve SOTA identity fidelity and temporal stability on a new large-scale dataset.