GVCCTurbo: Rate-Compute Quality Scheduling for Codebook Driven Generative Compression

Trajectory-Consistent Denoising Diffusion Codebook Models for Zero-Shot High-Fidelity Image Compression at Ultra-Low Bitrates

ReGenVC: End-to-End Real-Time Generative Video Coding at Ultra-Low Bitrate

ReGenVC is the first end-to-end generative video codec to combine ultra-low-bitrate encoding with real-time decoding on an 8-GPU system and three model-preserving system techniques are presented.

GVC-RT: Towards Real-Time Generative Video Compression at Ultra-Low Bitrates

This work systematically identifies the computational bottlenecks and proposes GVC-RT, which redesigns the generative latent coding framework to realize real-time video coding without sacrificing compression performance, and introduces a lightweight de-tokenizer architecture to resolve the final latency bottleneck during decoding.

MixCompress: Mixture of Experts for Variable Rate Learned Image Compression

This work proposes MixCompress, a unified VBR framework based on sparse structural specialization that not only matches individually optimized single-rate baselines but can even surpass them, establishing a new Pareto frontier for computationally efficient image coding.

A Generalized Slimmable INR Framework for Scalable Video Coding

Implicit neural representations (INRs) encode video frames as network weights, offering a new compression paradigm. A persistent limitation is that existing INR codecs train one model per target bitrate, so multi-rate deployment needs separate runs, separate checkpoints, and model reloading, costs that grow with the number of rate points. We propose a Generalized Slimmable Framework that replaces standard layers with width-configurable counterparts in most INR decoders, letting a single checkpoint serve multiple bitrates through nested weight tensors without topology changes. To recover the quality lost in shared-weight training, we introduce Slimmable Conditional Decoder Modulation (SCDM), which blends slimmable expert convolutions via width- and frame-conditioned gating. For encoder-based backbones, encoder output caching separates encoder computation from multi-width training. Across four INR backbones on DAVIS and Bunny, the framework cuts multi-rate storage by about 2.3–2.5×, and SCDM recovers substantial quality at all rate points while surpassing independently trained fixed-width models at narrow widths.

VoRTeC: Taming Foundation Flow for One-step Real time Video Compression

Ultra-low bitrate video compression still faces critical challenges: traditional neural video compression inevitably introduces blurring artifacts, while diffusion-based generative video compression suffers from excessive decoding latency and poor temporal consistency. To address these issues, we propose $\mathtt{VoRTeC}$, a Video Compression framework built upon a foundational flow model (Wan2.1). By compactly encoding latent video representations, predicting the positions of compressed representations along flow trajectories, and integrating multi-scale priors, $\mathtt{VoRTeC}$ enables the compressor to harness generative video flow priors effectively. Without accessing the parameters or gradients of flow matching networks, our framework achieves one-step decoding and reconstructions with high perceptual fidelity. Meanwhile, we maintain consistency across frame groups via tail-frame reuse and prior caching. Extensive experiments demonstrate that our method reduces bit consumption by 58\% compared to prior diffusion-based approaches, with decoding speed boosted by 3 to 197 times: $\mathtt{VoRTeC}$ achieves a decoding speed of 13 FPS at 720p and 32 FPS at 480p.