arXiv Computer Vision
Keyframe-Centric State-Space Modeling for Burst Image Super-Resolution
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FlashDecoder: Real-Time Latent-to-Pixel Streaming Decoder with Transformers
arXiv:2607. 14898v1 Announce Type: cross Abstract: Real-time video generation demands fast decoding as much as fast denoising, yet current latent video diffusion models rely on 3D convolutional decoders that are slow and memory-intensive at high resolutions or for long video.
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PixelIR: Fidelity-Perception Decoupling via Pixel-Space Image-Residual Flow Matching for Efficient One-Step Real-World Super-Resolution
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Learning to Adaptively Allocate Gaussians for Arbitrary-Scale Image Super-Resolution
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Detecting AI-Generated Videos with Spiking Neural Networks
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FMA-Net++: Motion- and Exposure-Aware Joint Video Super-Resolution and Deblurring
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VoRTeC: Taming Foundation Flow for One-step Real time Video Compression
The paper introduces VoRTeC, a video compression framework that leverages a foundational flow model to encode latent video representations compactly and predict their positions along flow trajectories. By integrating multi‑scale priors and avoiding access to flow‑matching network parameters, VoRTeC achieves one‑step decoding with high perceptual fidelity, while maintaining temporal consistency through tail‑frame reuse and prior caching. Experiments show a 58% reduction in bit consumption compared to prior diffusion‑based methods and a decoding speed increase ranging from 3 to 197 times, reaching 13 FPS at 720p and 32 FPS at 480p.