GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow

arXiv:2603.26571 · cs.CV, cs.AI · Submitted 2026-03-27 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-03-27

Updated: 2026-09-25

Comments: 9 pages, 3 figures

Code: https://github.com/CCdydy/GVCC

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Diffusion Model — 11× in this paper · explained in 8 episodes
• Generative Model — 11× in this paper · explained in 2 episodes
• Diffusion Models — 10× in this paper · explained in 6 episodes
• Fidelity — 10× in this paper · explained in 2 episodes
• Rectified Flow — 8× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Flow Matching — 4× in this paper · explained in 8 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Generative Video Codebook Codec — 3× in this paper
• Group of Pictures — 3× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes

Sources

• Generative Adversarial Networks for Extreme Learned Image Compression
• Building Normalizing Flows with Stochastic Interpolants
• Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
• Variational image compression with a scale hyperprior
• CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• Towards image compression with perfect realism at ultra-low bitrates
• Generative Video Compression: Towards 0.01% Compression Rate for Video Transmission
• ArcFace: Additive Angular Margin Loss for Deep Face Recognition
• Generative Latent Video Compression
• Denoising Diffusion Probabilistic Models
• High-Fidelity Image Compression with Score-based Generative Models
• CoD: A Diffusion Foundation Model for Image Compression
• Free-GVC: Towards Training-Free Extreme Generative Video Compression with Temporal Coherence
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
• Generative Neural Video Compression via Video Diffusion Prior
• Joint Autoregressive and Hierarchical Priors for Learned Image Compression
• Scalable Diffusion Models with Transformers
• Denoising Diffusion Implicit Models

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