# Dense-Prior-Guided Generative Video Compression

arXiv: https://arxiv.org/abs/2604.06655 · HTML: https://mlsanity.com/paper/2604.06655/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 22 of 22)

- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [VACE: All-in-One Video Creation and Editing](https://mlsanity.com/paper/2503.07598/) (2025)
- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Effective Whole-body Pose Estimation with Two-stages Distillation](https://mlsanity.com/paper/2307.15880/) (2023)
- [Neural Video Compression with Diverse Contexts](https://mlsanity.com/paper/2302.14402/) (2023)
- [Neural Video Compression with Feature Modulation](https://mlsanity.com/paper/2402.17414/) (2024)
- [Controllable Video Generation: A Survey](https://mlsanity.com/paper/2507.16869/) (2025)
- [Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models](https://mlsanity.com/paper/2301.11189/) (2023)
- [Learning to generate line drawings that convey geometry and semantics](https://mlsanity.com/paper/2203.12691/) (2022)
- [Perceptual Learned Video Compression with Recurrent Conditional GAN](https://mlsanity.com/paper/2109.03082/) (2021)
- [Neural Video Compression using GANs for Detail Synthesis and Propagation](https://mlsanity.com/paper/2107.12038/) (2021)
- [Diffusion-based Perceptual Neural Video Compression with Temporal Diffusion Information Reuse](https://mlsanity.com/paper/2501.13528/) (2025)
- [I2VC: A Unified Framework for Intra- & Inter-frame Video Compression](https://mlsanity.com/paper/2405.14336/) (2024)
- [M3-CVC: Controllable Video Compression with Multimodal Generative Models](https://mlsanity.com/paper/2411.15798/) (2024)
- [T-GVC: Trajectory-Guided Generative Video Coding at Ultra-Low Bitrates](https://mlsanity.com/paper/2507.07633/) (2025)
- [Extreme Video Compression with Pre-trained Diffusion Models](https://mlsanity.com/paper/2402.08934/) (2024)
- [Conditional Video Generation for High-Efficiency Video Compression](https://mlsanity.com/paper/2507.15269/) (2025)
- [Diffusion-aided Extreme Video Compression with Lightweight Semantics Guidance](https://mlsanity.com/paper/2602.05201/) (2026)
- [Bidirectional Temporal Diffusion Model for Temporally Consistent Human Animation](https://mlsanity.com/paper/2307.00574/) (2023)

## Built on by (most cited first, 1 of 1)

- [GVCCTurbo: Rate-Compute Quality Scheduling for Codebook Driven Generative Compression](https://mlsanity.com/paper/2608.03517/) (2026)

## Abstract (arXiv)

Diffusion-based generative video compression offers a promising paradigm for low-bitrate reconstruction, but existing keyframe-based controllable approaches rely on sparse priors for non-keyframes, which often lack sufficient low-level details for faithful reconstruction and require non-trivial training to adapt generative models to specialized controls. We propose Dense-Prior-Guided Generative Video Compression (DGVC), a training-free framework that uses codec-compressed luminance components of non-keyframes as dense priors to guide reconstruction. Unlike sparse controls, the dense luminance priors are compatible with modern fidelity-oriented video codecs and remain sufficiently faithful after compression, eliminating the need for prior-specific codec training and making additional generative-model adaptation unnecessary in practice. Moreover, luminance priors preserve richer structural and textural information, thereby providing more reliable guidance for controllable video generation. DGVC further introduces a color-distance-guided keyframe selection strategy to capture color variations for chrominance recovery. Extensive experiments show that DGVC consistently surpasses prior controllable compression methods in both signal fidelity and perceptual quality, while remaining competitive with representative post-enhancement approaches.