arxiv: 2604.06655 · v1 · submitted 2026-04-08 · 💻 cs.CV

Recognition: no theorem link

Controllable Generative Video Compression

Ding Ding , Daowen Li , Ying Chen , Yixin Gao , Ruixiao Dong , Kai Li , Li Li

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:19 UTC · model grok-4.3

classification 💻 cs.CV

keywords video compressiongenerative video modelingperceptual qualitysignal fidelitykeyframe selectioncontrollable generationstructural priors

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The pith

Controllable Generative Video Compression maintains signal fidelity and perceptual quality by guiding a generative model with coded keyframes and dense per-frame controls

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

Perceptual video compression often gains visual realism by using generative models yet loses accuracy in reproducing the original signal. This paper introduces the Controllable Generative Video Compression paradigm to close that gap. Representative keyframes are coded to supply structural priors for the scene, while dense per-frame control priors are also coded to capture finer structure and semantics. A controllable video generation model then reconstructs the non-keyframes under these guides, enforcing temporal and content consistency. A separate color-distance-guided algorithm chooses the keyframes adaptively so that color information is recovered accurately.

Core claim

The central claim is that coding keyframes for structural priors, adding coded dense per-frame controls, and feeding both into a controllable video generation model allows non-keyframes to be reconstructed with temporal and content consistency; the color-distance-guided keyframe selection further ensures accurate color recovery, so that the overall method exceeds prior perceptual video compression techniques on both signal-fidelity metrics and perceptual-quality metrics.

What carries the argument

The controllable video generation model that receives structural priors from coded keyframes and dense per-frame control priors to reconstruct non-keyframes while preserving consistency

If this is right

Both objective signal metrics and subjective perceptual scores can improve together instead of trading off against each other.
Non-keyframes recover finer structure, semantics, and color more reliably because of the added dense controls and adaptive keyframe choice.
Temporal consistency across the sequence is maintained by the guided generation process even when most frames are synthesized rather than transmitted directly.
Compression systems can code fewer full frames while still achieving high-fidelity reconstruction of the remaining frames.

Where Pith is reading between the lines

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

The same control-prior approach could be tested on longer sequences or higher frame-rate content to check whether consistency holds at scale.
Hybrid pipelines that combine this guided generation with conventional codecs might further reduce bitrate for a target quality level.
The color-distance keyframe rule could be replaced by a learned selector without changing the rest of the reconstruction pipeline.

Load-bearing premise

The generative model will faithfully recover the original details, colors, and structures from the supplied priors without introducing artifacts or temporal inconsistencies that would reduce signal fidelity.

What would settle it

A direct comparison of CGVC against a prior perceptual compression baseline on standard video test sets at matched bitrates, checking whether CGVC simultaneously raises PSNR or SSIM for fidelity and improves perceptual scores such as LPIPS or human preference ratings

Figures

Figures reproduced from arXiv: 2604.06655 by Daowen Li, Ding Ding, Kai Li, Li Li, Ruixiao Dong, Ying Chen, Yixin Gao.

**Figure 2.** Figure 2: Uniformly selected keyframes and a reconstructed intermediate non-keyframe. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Rate and perception/fidelity curves on the HEVC and MCL-JCV datasets. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Visual comparisons with baselines on the [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Variation in BD-rate (%) as Wmax and τ are modulated. map and edge map are also compared. It’s obvious that the luminance component generally surpasses the other two types of control prior in terms of content fidelity. As for color correction, all metrics degrade consistently after being removed, demonstrating the validity of the color correction. Hyper-parameters of the keyframe selection algorithm are al… view at source ↗

**Figure 6.** Figure 6: Color-distance-guided selected keyframes and the corresponding reconstructed non-keyframe. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗

**Figure 7.** Figure 7: Rate and perception/fidelity curves on the UVG dataset. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗

**Figure 8.** Figure 8: Visual comparisons with baselines on the [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗

**Figure 9.** Figure 9: Visual comparisons with baselines on the [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗

**Figure 10.** Figure 10: Visual comparisons with baselines on the [PITH_FULL_IMAGE:figures/full_fig_p009_10.png] view at source ↗

**Figure 11.** Figure 11: Visual comparisons of color correction on the [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗

**Figure 12.** Figure 12: Frame-by-frame comparisons on the KristenAndSara sequence in the HEVC Class E dataset [PITH_FULL_IMAGE:figures/full_fig_p010_12.png] view at source ↗

**Figure 13.** Figure 13: Frame-by-frame comparisons on the videoSRC30 sequence in the MCL-JCV dataset [PITH_FULL_IMAGE:figures/full_fig_p011_13.png] view at source ↗

read the original abstract

Perceptual video compression adopts generative video modeling to improve perceptual realism but frequently sacrifices signal fidelity, diverging from the goal of video compression to faithfully reproduce visual signal. To alleviate the dilemma between perception and fidelity, in this paper we propose Controllable Generative Video Compression (CGVC) paradigm to faithfully generate details guided by multiple visual conditions. Under the paradigm, representative keyframes of the scene are coded and used to provide structural priors for non-keyframe generation. Dense per-frame control prior is additionally coded to better preserve finer structure and semantics of each non-keyframe. Guided by these priors, non-keyframes are reconstructed by controllable video generation model with temporal and content consistency. Furthermore, to accurately recover color information of the video, we develop a color-distance-guided keyframe selection algorithm to adaptively choose keyframes. Experimental results show CGVC outperforms previous perceptual video compression method in terms of both signal fidelity and perceptual quality.

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.

Desk Editor's Note private letter to a colleague

CGVC adds color-guided keyframes and dense controls to generative video compression to chase both fidelity and perception, but the outperformance claim is still unverified.

read the letter

The main takeaway is that this paper puts forward a controllable generative video compression setup that codes keyframes for structural priors, adds dense per-frame control signals, and uses color-distance to pick those keyframes. The goal is to let a generative model fill in the rest while keeping better signal fidelity than typical perceptual methods. That specific mix of mechanisms is the clearest new piece, and it directly targets the usual drift and color-shift problems in generative reconstruction of non-keyframes. The description of how the priors are supposed to enforce temporal and content consistency is straightforward and practical. The color-distance selection is a sensible engineering choice that could help with one of the more visible failure modes in these systems. On the whole the approach feels like a reasonable incremental step rather than a radical departure, and the authors are clear about the perception-fidelity tension they are trying to ease. The soft spot is the lack of any reported numbers, baselines, or ablations in the abstract. The claim that CGVC beats prior perceptual methods on both fidelity and quality therefore sits on an assumption that the controllable generation will recover details faithfully without new artifacts or inconsistencies. Generative video models often fail exactly on that point even with conditioning, so the dual improvement result needs the actual metrics and comparisons to be taken as established. Without seeing those, it is difficult to judge whether the added controls deliver what is promised or whether the gains are mainly perceptual at the expense of fidelity after all. This is the kind of work that would interest people already following perceptual and generative video codecs. A reader who cares about the practical tradeoff in bandwidth-limited applications could pick up useful ideas about control signals and keyframe selection, even if they end up running their own tests. I would send it for peer review because the paradigm is distinct enough and the problem matters, but the authors should expect the referees to focus hard on the experimental evidence and any ablations that show the controls actually help fidelity.

Referee Report

2 major / 1 minor

Summary. The paper presents Controllable Generative Video Compression (CGVC), which codes representative keyframes selected via a color-distance-guided algorithm to serve as structural priors, additionally codes dense per-frame control priors, and reconstructs non-keyframes using a controllable generative video model to ensure temporal and content consistency. The key claim is that this method achieves better performance than previous perceptual video compression approaches in both signal fidelity and perceptual quality.

Significance. Should the results be confirmed through rigorous experimentation, the work would be significant in the field of video compression as it proposes a way to leverage controllable generative models to mitigate the typical trade-off between perceptual realism and signal fidelity, potentially influencing future codec designs.

major comments (2)

[Abstract] Abstract: The assertion that 'Experimental results show CGVC outperforms previous perceptual video compression method in terms of both signal fidelity and perceptual quality' is made without any quantitative metrics (e.g., PSNR/SSIM for fidelity or LPIPS/FID for perception), baseline methods, datasets, or evaluation protocol, rendering the central dual-outperformance claim unverifiable from the manuscript text.
[Description of the CGVC paradigm] Description of the CGVC paradigm: The claim of improved signal fidelity rests on the untested assumption that the controllable video generation model, conditioned on coded keyframes and dense per-frame controls, recovers details and colors without introducing artifacts, temporal drift, or inconsistencies; no ablations on the contribution of the per-frame controls, no fidelity-specific analysis, and no discussion of known generative failure modes (e.g., color shifts) are provided to support this load-bearing assumption.

minor comments (1)

[Abstract] The abstract is dense and would benefit from a single sentence clarifying the specific controllable video generation architecture employed and the exact form of the dense per-frame control prior.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below and indicate the revisions made to strengthen the presentation of our results and claims.

read point-by-point responses

Referee: [Abstract] The assertion that 'Experimental results show CGVC outperforms previous perceptual video compression method in terms of both signal fidelity and perceptual quality' is made without any quantitative metrics (e.g., PSNR/SSIM for fidelity or LPIPS/FID for perception), baseline methods, datasets, or evaluation protocol, rendering the central dual-outperformance claim unverifiable from the manuscript text.

Authors: We agree that the abstract, as a high-level summary, does not include the specific quantitative details needed to immediately verify the claim. The full manuscript (Section 4) reports the complete experimental results, including PSNR and SSIM for signal fidelity, LPIPS and FID for perceptual quality, comparisons against prior perceptual video compression baselines, standard datasets, and the evaluation protocol. We have revised the abstract to briefly reference the key metrics, baselines, and datasets to make the central claim more verifiable while remaining within length constraints. revision: yes
Referee: [Description of the CGVC paradigm] The claim of improved signal fidelity rests on the untested assumption that the controllable video generation model, conditioned on coded keyframes and dense per-frame controls, recovers details and colors without introducing artifacts, temporal drift, or inconsistencies; no ablations on the contribution of the per-frame controls, no fidelity-specific analysis, and no discussion of known generative failure modes (e.g., color shifts) are provided to support this load-bearing assumption.

Authors: We acknowledge the need for more explicit empirical support for the fidelity claim. The main results already include quantitative fidelity metrics showing improvement over baselines, and the color-distance-guided keyframe selection is intended to aid accurate color recovery. However, we agree that dedicated ablations, fidelity-focused analysis, and discussion of generative failure modes would strengthen the paper. We have added an ablation study on the contribution of the dense per-frame control priors, additional fidelity-specific visualizations and analysis, and a discussion of potential issues such as color shifts and temporal drift (including how the proposed priors mitigate them) in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces the CGVC paradigm by combining standard video coding of keyframes (for structural priors) and per-frame controls with an off-the-shelf controllable generative video model for non-keyframe reconstruction, plus a color-distance-guided keyframe selection heuristic. No equations, first-principles derivations, or fitted parameters are presented that reduce to the target result by construction. Claims of improved fidelity and perceptual quality rest on experimental comparisons rather than any self-referential prediction or self-citation chain. The approach is self-contained against external generative models and coding techniques, with no load-bearing uniqueness theorems or ansatzes imported from the authors' prior work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides insufficient detail to enumerate free parameters, axioms, or invented entities; no explicit hyperparameters, unproven assumptions, or new postulated entities are named.

pith-pipeline@v0.9.0 · 5457 in / 1090 out tokens · 45234 ms · 2026-05-10T18:19:22.479926+00:00 · methodology

discussion (0)

Reference graph

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