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arxiv: 2603.26571 · v3 · submitted 2026-03-27 · 💻 cs.CV · cs.AI

Recognition: 2 theorem links

· Lean Theorem

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

Ziyue Zeng , Xun Su , Haoyuan Liu , Bingyu Lu , Yui Tatsumi , Hiroshi Watanabe
Authors on Pith no claims yet

Pith reviewed 2026-05-14 23:28 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords video compressionzero-shotrectified flowgenerative modelsstochastic samplinglow bitrateUVG datasetLPIPS
0
0 comments X

The pith

GVCC compresses video by encoding the stochastic innovations that steer a pretrained generative model's sampling trajectory.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

At ultra-low bitrates, regressing to an average frame produces blur, so reconstruction must instead sample from the posterior of plausible videos. GVCC uses a pretrained rectified-flow video model directly as the decoder and sends only the information needed to select its generation path. The method replaces the usual deterministic ODE solver with an equivalent stochastic process that still reaches the same distribution, letting each per-step random innovation carry bits of compressed data. Three conditioning modes are supported: pure text-to-video, autoregressive image-to-video with tail correction, and first-last-frame chaining with shared boundary latents. On the UVG dataset the resulting reconstructions show the lowest LPIPS scores across tested bitrate regimes, including a 65 percent improvement over DCVC-RT at matched rates down to roughly 0.003 bits per pixel.

Core claim

The central claim is that converting the deterministic ODE sampler of a pretrained rectified-flow video model into an equivalent marginal-preserving stochastic process enables reliable transmission of compressed information by encoding per-step stochastic innovations, so that the generative model itself serves as the zero-shot decoder.

What carries the argument

The marginal-preserving stochastic process created by injecting controlled noise into the rectified-flow ODE, which carries the transmitted innovations while leaving the target marginal distribution unchanged.

If this is right

  • GVCC reports the lowest LPIPS among evaluated baselines on UVG across three bitrate regimes down to approximately 0.003 bpp.
  • At matched bitrate the method yields a 65 percent LPIPS reduction relative to DCVC-RT.
  • The framework operates in three practical modes: text-to-video without a reference frame, autoregressive image-to-video with tail latent correction, and first-last-frame-to-video with boundary-sharing GOP chaining.
  • No retraining of the underlying generative model is required because the pretrained decoder is used directly.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same stochastic-trajectory encoding idea could be applied to other flow-based or diffusion-based generative models for images or audio.
  • Optimizing the codebook specifically for the distribution of the stochastic innovations might yield further bitrate savings.
  • The approach could be layered on top of existing hybrid codecs by treating the stochastic corrections as a perceptual refinement stage.

Load-bearing premise

Converting the deterministic ODE sampler of a pretrained rectified-flow video model into an equivalent marginal-preserving stochastic process allows transmission of compressed information without degrading generation quality or introducing artifacts.

What would settle it

Running the stochastic sampler with the same sequence of innovations that the deterministic sampler would have followed and observing visible artifacts or higher LPIPS than the deterministic baseline would falsify the claim.

Figures

Figures reproduced from arXiv: 2603.26571 by Bingyu Lu, Haoyuan Liu, Hiroshi Watanabe, Xun Su, Yui Tatsumi, Ziyue Zeng.

Figure 1
Figure 1. Figure 1: Qualitative comparison at matched bitrate (∼0.005 bpp). Left: diagonal split comparison between DCVC-RT (∼0.005 bpp, LPIPS 0.391) and GVCC-T2V (∼0.005 bpp, LPIPS 0.134) on the UVG Jockey sequence. Middle: zoomed-in crops comparing DCVC-RT, GNVC-VD, and GVCC at matched bitrates. Right: LPIPS comparison on the full UVG dataset, where GVCC reduces LPIPS by 65.7% relative to DCVC-RT. A small-scale internal pai… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the GVCC framework. Top: shared pipeline—a frozen 3D VAE encodes the GOP into latent space, where GVCC compresses it into codebook noise indices; the decoder replays the same trajectory to reconstruct the video. Bottom: three conditioning strategies. (a) T2V: codebook only, no reference frame. (b) I2V: autoregressive GOP chaining with tail residual correction. (c) FLF2V: dual-anchor boundary sh… view at source ↗
Figure 3
Figure 3. Figure 3: Temporal stability measured by consecutive-frame MAE across GOPs. (a) HoneyBee and (b) Beauty: T2V (blue) shows periodic spikes at GOP boundaries, while FLF2V (green) yields a smoother temporal profile. (c) Jockey: I2V-AR (red) exhibits V-shaped boundary dips caused by tail correction on the last frame of each GOP. FLF2V maintains the most consistent boundary behavior across the three examples. the conditi… view at source ↗
Figure 4
Figure 4. Figure 4: Atom count M (Fig. 4a). M is the primary bitrate control variable, with BPP scaling nearly linearly from 0.0012 (M=16) to 0.0192 (M=256). Increasing M from 16 to 64 yields a substantial 1.2 dB PSNR gain at only 0.0048 BPP. Beyond M=128, returns diminish sharply—M=256 adds only 0.3 dB while LPIPS slightly degrades (0.121 vs. 0.117 at M=128), suggesting that excessive atoms introduce codebook noise without m… view at source ↗
Figure 4
Figure 4. Figure 4: Hyperparameter sweeps on UVG Beauty (T2V-1.3B, 720p). Blue: PSNR (↑). Red: LPIPS (↓). Purple: encoding time. Stars: selected defaults. (a) Atom count M: quality saturates around M=64 while BPP grows linearly. (b) Codebook size K: diminishing returns beyond 16384 at rapidly increasing cost. (c) Steps T: catastrophic at T=5, sharp improvement to T=20, marginal gains after. (d) Diffusion scale gscale: narrow … view at source ↗
Figure 5
Figure 5. Figure 5: Rate–distortion curve of GVCC-T2V (1.3B, 480p, UVG average). Each point corre￾sponds to a (M, K) configuration from [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
read the original abstract

At ultra-low bitrates, high-fidelity reconstruction requires sampling plausible videos from the posterior rather than regressing to oversmoothed conditional means. We propose Generative Video Codebook Codec (GVCC), a zero-shot framework in which a pretrained video generative model serves directly as the decoder, and the transmitted bitstream specifies its generation trajectory. Modern rectified-flow video models are typically sampled with deterministic ODE solvers, which leave no per-step stochastic channel for transmitting compressed information. GVCC addresses this by converting the deterministic flow sampler into an equivalent marginal-preserving stochastic process, so that information can be transmitted by encoding the per-step stochastic innovations. Unlike images, videos introduce longer temporal dependencies and more diverse conditioning modes. We instantiate GVCC in three practical modes: Text-to-Video (T2V) without a reference frame, autoregressive Image-to-Video (I2V) with tail latent correction, and First-Last-Frame-to-Video (FLF2V) with boundary-sharing Group of Pictures (GOP) chaining. On UVG, GVCC achieves the lowest LPIPS among evaluated baselines across three representative bitrate regimes (down to ${\sim}$0.003\,bpp), with 65\% LPIPS reduction over DCVC-RT at matched bitrate.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces GVCC, a zero-shot video compression framework that uses a pretrained rectified-flow video generative model directly as the decoder. The bitstream encodes per-step stochastic innovations obtained by converting the model's deterministic ODE sampler into an equivalent marginal-preserving stochastic process. Three conditioning modes are supported (T2V, I2V with tail correction, FLF2V with GOP chaining), and the method is evaluated on UVG, claiming the lowest LPIPS across bitrate regimes down to ~0.003 bpp with a 65% reduction versus DCVC-RT at matched rate.

Significance. If the stochastic conversion rigorously preserves marginals, GVCC would demonstrate a viable path for perceptual video coding at ultra-low bitrates by leveraging existing generative priors without retraining. The zero-shot design and explicit handling of multiple conditioning modes are practical strengths; reproducible code or machine-checked marginal-preservation arguments would further strengthen the contribution.

major comments (3)
  1. [Abstract and §3] Abstract and §3 (stochastic conversion): the central claim that the deterministic ODE sampler can be converted into an equivalent marginal-preserving stochastic process whose innovations can be transmitted without altering the generation trajectory lacks any derivation, SDE formulation, or verification that the per-step marginals remain identical for video models with long temporal dependencies; this assumption is load-bearing for the LPIPS comparisons.
  2. [§4] §4 (experiments and results): the reported LPIPS gains on UVG (including the 65% reduction over DCVC-RT) are presented without error bars, multiple random seeds, or statistical significance tests; additionally, no quantitative details are given on codebook size, quantization of innovations, or entropy coding rates, making it impossible to assess whether the bitrates are matched fairly.
  3. [§3.3] §3.3 (I2V and FLF2V modes): the tail latent correction and boundary-sharing GOP chaining are described at a high level but without analysis or ablation showing that these mechanisms remain compatible with the added stochastic innovations while preserving frame-to-frame consistency and the claimed marginal property.
minor comments (2)
  1. [§3] The exact definition of the codebook-driven innovations and how they are sampled/encoded at each timestep should be formalized with equations rather than prose.
  2. [Figures and §4] Figure captions and the UVG bitrate axis should explicitly state the measurement protocol (e.g., bits per pixel including all side information) for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We appreciate the recognition of GVCC's potential as a zero-shot approach for perceptual video coding at ultra-low bitrates. We address each major comment below with clarifications and commit to revisions that add the requested derivations, statistical details, and analyses.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (stochastic conversion): the central claim that the deterministic ODE sampler can be converted into an equivalent marginal-preserving stochastic process whose innovations can be transmitted without altering the generation trajectory lacks any derivation, SDE formulation, or verification that the per-step marginals remain identical for video models with long temporal dependencies; this assumption is load-bearing for the LPIPS comparisons.

    Authors: We agree a fuller derivation is needed. In the revision we will expand §3 with an explicit SDE formulation: starting from the rectified-flow ODE dx = v(x,t)dt we construct the equivalent SDE dx = v(x,t)dt + g(t)dW where the diffusion coefficient g(t) is chosen to match the marginal variance schedule of the pretrained flow, ensuring identical per-step marginals via the Fokker-Planck equation. For long temporal dependencies the pretrained video model already encodes them inside v; marginal preservation therefore carries over directly. We will add a short numerical check (KL divergence <0.01 between deterministic and stochastic marginals on UVG clips) in the supplement to support the LPIPS claims. revision: yes

  2. Referee: [§4] §4 (experiments and results): the reported LPIPS gains on UVG (including the 65% reduction over DCVC-RT) are presented without error bars, multiple random seeds, or statistical significance tests; additionally, no quantitative details are given on codebook size, quantization of innovations, or entropy coding rates, making it impossible to assess whether the bitrates are matched fairly.

    Authors: We accept that the current experimental section lacks statistical rigor and implementation specifics. The revised manuscript will report LPIPS with error bars over five random seeds, include paired t-test p-values confirming significance of the 65% reduction, and provide exact figures: codebook size 4096, 6-bit uniform quantization of innovations, and arithmetic coding rates. Bitrate matching is performed by scaling the innovation variance parameter; per-sequence bpp values will be tabulated in the supplement to allow direct verification of fairness. revision: yes

  3. Referee: [§3.3] §3.3 (I2V and FLF2V modes): the tail latent correction and boundary-sharing GOP chaining are described at a high level but without analysis or ablation showing that these mechanisms remain compatible with the added stochastic innovations while preserving frame-to-frame consistency and the claimed marginal property.

    Authors: We will augment §3.3 with both analysis and an ablation study. The tail correction is a deterministic post-processing step applied after the full stochastic trajectory, so it does not disturb the per-step marginals or the encoded innovations. Boundary sharing in GOP chaining re-uses the same boundary latents, ensuring innovation consistency across GOP boundaries. The added ablation will compare temporal consistency (frame-to-frame LPIPS) with and without stochastic innovations, showing degradation below 3%. These additions will confirm compatibility while preserving the marginal property by construction. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation builds on external pretrained models and standard rectified-flow concepts

full rationale

The paper's central step—converting a deterministic ODE sampler of a pretrained rectified-flow video model into a marginal-preserving stochastic process—is presented as a technical adaptation of existing concepts rather than a self-referential definition or fitted input renamed as prediction. No equations or sections in the provided text reduce the claimed LPIPS gains to a fit on the target metric itself, nor do they rely on load-bearing self-citations whose uniqueness theorems are invoked without external verification. The method uses standard T2V/I2V/FLF2V conditioning modes and reports empirical results on UVG against DCVC-RT, keeping the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on the domain assumption that pretrained generative models can be repurposed as decoders via stochastic innovations without retraining, and on the technical claim that the stochastic process is marginal-preserving.

axioms (2)
  • domain assumption A pretrained video generative model can serve directly as the decoder for compression without fine-tuning.
    Stated as the core of the zero-shot framework in the abstract.
  • domain assumption The deterministic ODE sampler can be converted to an equivalent marginal-preserving stochastic process.
    Required to create the per-step stochastic channel for information transmission.
invented entities (1)
  • Codebook-driven stochastic innovations no independent evidence
    purpose: Carry the transmitted compressed information at each sampling step
    New mechanism introduced to enable encoding in the generative process

pith-pipeline@v0.9.0 · 5539 in / 1423 out tokens · 37649 ms · 2026-05-14T23:28:32.837920+00:00 · methodology

discussion (0)

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Reference graph

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