mlsanityData to 14 Oct 2026

Latent-Frequency Validity: Fast Spectral Editing with Screened Video-VAE Transfer Operators

arXiv 2608.07569 ↗First posted 4 Aug 2026cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

Direct spectral editing in video-VAE latents can control noise, flicker, smoothness, and frequency content without a decode--filter--reencode pass. However, video VAEs may redistribute pixel-space frequency bands across latent channels, and latent edits can disrupt VAE round-trip dynamics. We introduce latent-frequency validity (LFV), which learns a compact VAE-specific spectral response and deploys it only when it improves decoded-target fidelity without worsening round-trip drift. LFV follows a validation-selected path from a diagonal per-frequency calibrator (C1) to full channel mixing (CM), making cross-channel capacity a controllable per-edit resource. Across 544 VAE--edit cells spanning six spectral families, LFV emits 423 cheap operators: 277 are handled by C1, while 146 (34.5% of emitted operators) require channel mixing. On the primary 120-cell radial sweep, 99/100 emitted operators pass source-video-grouped held-out evaluation. Across five additional filter families, all 323 emitted operators pass held-out evaluation. Fully frozen OpenVid-fitted operators, including the validation-selected path coefficient, pass all 20 tested CogVideoX and HunyuanVideo generated-domain cells without adaptation. The selected response matches direct latent-filter latency and is about 3× faster than pixel filter--reencode. The resulting maps reveal distinct VAE regimes, including strongly channel-coupled CogVideoX responses and a sharp Open-Sora high-band stability frontier.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 22 references that are in the map. First posted on arXiv 4 Aug 2026.

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18 of 22 references.

References · 18

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  2. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
  3. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,708
  4. 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,822
  5. 2024Open-Sora: Democratizing Efficient Video Production for Allcited by 652
  6. 2024OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generationcited by 341
  7. 2021Global Filter Networks for Image Classificationcited by 260
  8. 2024CV-VAE: A Compatible Video VAE for Latent Generative Video Modelscited by 70
  9. 2024WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Modelcited by 57
  10. 2025FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editingcited by 12
  11. 2025Delving into Latent Spectral Biasing of Video VAEs for Superior Diffusabilitycited by 11
  12. 2025Toward Diffusible High-Dimensional Latent Spaces: A Frequency Perspectivecited by 10
6 more
  1. 2025FADE: Frequency-Aware Diffusion Model Factorization for Video Editingcited by 7
  2. 2026Spectrum Matching: a Unified Perspective for Superior Diffusability in Latent Diffusioncited by 7
  3. 2025FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editingcited by 6
  4. 2025Your Latent Mask is Wrong: Pixel-Equivalent Latent Compositing for Diffusion Modelscited by 4
  5. 2026Why Do DiT Editors Drift? Plug-and-Play Low Frequency Alignment in VAE Latent Spacecited by 3
  6. 2025FreSca: Scaling in Frequency Space Enhances Diffusion Modelscited by 2

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