arxiv: 2604.14648 · v1 · submitted 2026-04-16 · 💻 cs.CV · cs.AI· cs.LG

Recognition: unknown

Seen-to-Scene: Keep the Seen, Generate the Unseen for Video Outpainting

Inseok Jeon , Minhyeok Lee , Seunghoon Lee , Minseok Kang , Suhwan Cho , Sangyoun Lee

Authors on Pith no claims yet

Pith reviewed 2026-05-10 12:08 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG

keywords video outpaintingtemporal coherenceflow completion networklatent propagationvideo generationdiffusion modelsmotion fieldsscene expansion

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The pith

Unifying flow propagation of seen content with latent generation for unseen areas improves temporal consistency in video outpainting

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

The paper proposes Seen-to-Scene as a framework to expand video content beyond original frame boundaries while preserving spatial fidelity and temporal coherence. It unifies propagation of existing content through an end-to-end fine-tuned flow completion network adapted from inpainting with reference-guided latent propagation for reliable spreading across frames. This hybrid approach targets inconsistencies that arise in purely generative methods like diffusion models, particularly in dynamic scenes or large expansions, and claims efficient inference without input-specific adaptation. A sympathetic reader would care because consistent video expansion supports applications in editing and content creation where motion artifacts currently limit usability.

Core claim

Seen-to-Scene unifies propagation-based and generation-based paradigms for video outpainting by leveraging a flow completion network pre-trained for video inpainting, which is fine-tuned in an end-to-end manner to bridge the domain gap and reconstruct coherent motion fields, together with reference-guided latent propagation that effectively propagates source content across frames, achieving superior temporal coherence and visual realism with efficient inference that surpasses prior state-of-the-art methods even those requiring input-specific adaptation.

What carries the argument

Flow completion network adapted from video inpainting via end-to-end fine-tuning, paired with reference-guided latent propagation for content spreading

If this is right

Expanded videos maintain spatial fidelity and inter-frame motion consistency without per-input retraining.
The method handles dynamic scenes and large expansion scenarios more reliably than generation-only approaches.
Inference remains efficient while delivering higher visual realism than prior state-of-the-art techniques.
Hybrid propagation reduces intra-frame and inter-frame inconsistencies that limit current video outpainting tools.

Where Pith is reading between the lines

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

The same adaptation strategy for flow networks might transfer to related video tasks such as interpolation or stabilization where motion coherence is central.
Longer sequences could benefit disproportionately because propagation limits error buildup that pure generation accumulates over many frames.
Real-time video editing pipelines might adopt this for speed gains if the fine-tuning proves stable across varied input resolutions.

Load-bearing premise

A flow completion network pre-trained for video inpainting can be fine-tuned end-to-end to accurately reconstruct coherent motion fields when applied to outpainting instead of inpainting, especially across dynamic scenes and large boundary expansions.

What would settle it

Experiments on videos with rapid object motion or large outpainting ratios that show increased flickering, motion discontinuities, or visual artifacts in the generated regions compared to pure diffusion baselines would indicate the fine-tuning fails to bridge the domain gap.

Figures

Figures reproduced from arXiv: 2604.14648 by Inseok Jeon, Minhyeok Lee, Minseok Kang, Sangyoun Lee, Seunghoon Lee, Suhwan Cho.

**Figure 1.** Figure 1: Seen-to-Scene is a novel framework that unifies propagation-based and generation-based paradigms for video outpainting. It maintains strong spatio-temporal consistency across frames even under challenging conditions such as camera motion, dynamic objects and complex motion patterns. The official implementation is released at https://github.com/InSeokJeon/Seen_to_Scene. Abstract Video outpainting aims to ex… view at source ↗

**Figure 2.** Figure 2: Computational cost comparison with a SOTA [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Key limitations of existing video outpainting approaches. (a) Flow completion domain gap: Flow completion networks trained for video inpainting struggle to generalize to video outpainting due to the substantially larger missing regions involved. Consequently, the predicted flow often suffers from bleeding around dynamic objects and fails to provide reliable motion estimates for regions far from the origina… view at source ↗

**Figure 4.** Figure 4: Overview of the proposed Seen-to-Scene framework. Seen-to-Scene unifies propagation and generation for coherent video outpainting. Given an input video, reference frames are selected and optical flow is estimated using RAFT [17], followed by flow completion with FCNet [28]. The input frames are encoded into latent representations via a pre-trained VAE encoder. Using the completed flow, we perform referenc… view at source ↗

**Figure 5.** Figure 5: Comparison of flow completion results between an in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Visualization of warping results using an inpainting pre [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 7.** Figure 7: Qualitative comparison between Seen-to-Scene and State-of-the-art methods. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

**Figure 8.** Figure 8: Effect of the temporal window size m on FVD. m = 4 in our method, which effectively balances temporal consistency and computational efficiency. 6. Conclusion and Future Work In this work, we present Seen-to-Scene, a novel end-to-end framework that unifies propagation-based and diffusionbased paradigms for video outpainting. We further introduce an efficient and effective reference-guided latent propagati… view at source ↗

**Figure 9.** Figure 9: Two settings of video inpainting: (a) Object Removal [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗

**Figure 12.** Figure 12: Comparison of fixed-stride and similarity-based reference selection. While the fixed-stride method selects frame (d) with [PITH_FULL_IMAGE:figures/full_fig_p010_12.png] view at source ↗

**Figure 13.** Figure 13: Visualization of flow completion results. Our outpainting fine-tuned model produces geometrically stable and semantically separated motion fields compared to inpainting-pretrained flow completion. coherent and extends seamlessly into the expanded regions. The fine-tuned model learns to infer long-range and crossboundary motion more robustly, resulting in stable propagation even for highly dynamic scenes… view at source ↗

**Figure 14.** Figure 14: Visualization of pixel-level warping using different flow completion networks. Our outpainting fine-tuned model achieves stable propagation, while the inpainting pre-trained model fails to propagate content effectively to outpainting regions. C.2. Latent Refinement To refine warped multi-frame latents for video outpainting, we introduce a dual-branch refinement module that operates directly on the warpe… view at source ↗

**Figure 15.** Figure 15: Visualization of propagation strategies. (a) Conventional sequential propagation accumulates flow across all intermediate frames, while (b) our reference-guided latent propagation performs efficient warping using only selected references in the latent space. FW Propagated Latent Original Latent Original VO Mask FW Propagated Region Mask 𝐶 CONV Offset Mask DCN 𝐶 CONV Offset Mask DCN CONV CONV 𝑆 𝑆 1 x 1 C… view at source ↗

**Figure 16.** Figure 16: Architecture of the latent refinement module. It refines propagated latent features through spatial alignment and residual correction, enhancing temporal consistency and visual coherence in the outpainted video. backward-propagated latents obtained from bi-directional warping, and then concatenate the two refined latents and pass them through a final convolutional layer to produce a unified, refined bi-d… view at source ↗

**Figure 17.** Figure 17: Qualitative results of our method across diverse scenarios and environments. The examples demonstrate the robustness and generalization capability of our model in handling various motion patterns, scene complexities, and outpainting configurations [PITH_FULL_IMAGE:figures/full_fig_p016_17.png] view at source ↗

**Figure 18.** Figure 18: Qualitative results across different spatial resolutions and aspect ratios. Our method maintains consistent visual quality and structure from 256 × 256 to extended formats such as 256 × 512 and 256 × 768 [PITH_FULL_IMAGE:figures/full_fig_p017_18.png] view at source ↗

read the original abstract

Video outpainting aims to expand the visible content of a video beyond the original frame boundaries while preserving spatial fidelity and temporal coherence across frames. Existing methods primarily rely on large-scale generative models, such as diffusion models. However, generationbased approaches suffer from implicit temporal modeling and limited spatial context. These limitations lead to intraframe and inter-frame inconsistencies, which become particularly pronounced in dynamic scenes and large outpainting scenarios. To overcome these challenges, we propose Seen-to-Scene, a novel framework that unifies propagationbased and generation-based paradigms for video outpainting. Specifically, Seen-to-Scene leverages flow-based propagation with a flow completion network pre-trained for video inpainting, which is fine-tuned in an end-to-end manner to bridge the domain gap and reconstruct coherent motion fields. To further improve the efficiency and reliability of propagation, we introduce a reference-guided latent propagation that effectively propagates source content across frames. Extensive experiments demonstrate that our method achieves superior temporal coherence and visual realism with efficient inference, surpassing even prior state-of-the-art methods that require input-specific adaptation.

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

The paper fine-tunes an inpainting flow network for video outpainting plus reference-guided latent propagation, but the abstract gives no numbers to back the coherence claims and the extrapolation step looks under-supported.

read the letter

The core idea is to take a flow completion network trained on video inpainting, fine-tune it end-to-end for outpainting, and add reference-guided latent propagation so that seen content stays stable while unseen areas get filled. This hybrid is presented as a way to avoid the temporal drift common in pure diffusion outpainting, especially in moving scenes or when the expansion is large. The abstract positions the approach as faster at inference and more coherent than prior generative baselines that need per-video adaptation. That combination of pre-trained flow plus targeted fine-tuning and latent guidance is the concrete technical step that is not already standard in the summarized prior work. It is a practical engineering move that reuses existing models rather than training everything from scratch. The paper correctly flags that generation-only methods struggle with consistency when context is missing on one side of the frame. The soft spot is that none of the performance claims are quantified in the abstract, and the central transfer from inpainting to outpainting is not obviously guaranteed. Inpainting supplies context on all sides; outpainting requires inventing motion beyond the boundary. Without details on the loss terms, architectural tweaks, or failure cases that show how fine-tuning actually produces plausible extrapolation, the superiority over diffusion baselines rests on an assumption that may not hold in dynamic or large-expansion cases. The experiments section will need to carry the weight here. This work is for people building video editing or XR tools who care about keeping existing pixels intact while extending the frame. A reader who wants a concrete hybrid recipe rather than another end-to-end diffusion model could pick up usable ideas from the propagation design. The paper deserves a serious referee because the problem is well-defined and the proposed components are specific enough to evaluate, even though the current evidence is thin. I would send it to review with instructions to add quantitative results, ablations on the fine-tuning, and direct comparisons on the extrapolation failure modes.

Referee Report

2 major / 2 minor

Summary. The paper proposes Seen-to-Scene, a video outpainting framework that unifies propagation-based and generation-based approaches by leveraging a flow completion network pre-trained on video inpainting, fine-tuned end-to-end to reconstruct coherent motion fields, combined with reference-guided latent propagation to expand frames while preserving spatial fidelity and temporal coherence, claiming superior results over diffusion models especially in dynamic scenes and large expansions.

Significance. If the experimental validation holds, the work offers a potentially more efficient alternative to large generative models for video outpainting by adapting propagation techniques, which could improve temporal consistency without requiring per-input adaptation.

major comments (2)

[Abstract] Abstract: the central claim that end-to-end fine-tuning of an inpainting-pretrained flow completion network successfully bridges the domain gap for outpainting extrapolation is asserted without any quantitative results, ablation details, or explicit mechanism (e.g., specialized loss terms or architectural modifications) to address the fundamental asymmetry between inpainting (surrounded context) and outpainting (one-sided missing context).
[Method] Method description (flow-based propagation): the assumption that a network trained for motion completion in inpainting can extrapolate coherent motions in dynamic scenes and large expansion ratios lacks supporting analysis or targeted experiments demonstrating robustness to the lack of outer reference pixels, which is load-bearing for the superiority claim over diffusion baselines.

minor comments (2)

[Abstract] Abstract: the phrase 'unifies propagation-based and generation-based paradigms' is used but the precise integration point between the flow propagation and any generative component is not clarified in the high-level description.
[Discussion] The paper would benefit from a dedicated limitations or failure-case discussion section, particularly for scenarios where motion extrapolation may break down.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment point by point below with clarifications from the manuscript and indicate planned revisions.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that end-to-end fine-tuning of an inpainting-pretrained flow completion network successfully bridges the domain gap for outpainting extrapolation is asserted without any quantitative results, ablation details, or explicit mechanism (e.g., specialized loss terms or architectural modifications) to address the fundamental asymmetry between inpainting (surrounded context) and outpainting (one-sided missing context).

Authors: The abstract summarizes the core contribution at a high level, as is standard. Quantitative results validating the fine-tuned network's performance (including temporal coherence metrics outperforming diffusion baselines in dynamic scenes) appear in Section 4, with ablation studies on the end-to-end fine-tuning in Section 4.2. The mechanism for handling asymmetry relies on the reference-guided latent propagation combined with flow consistency losses during fine-tuning, which adapt the pre-trained inpainting model to extrapolate from one-sided seen context; this is detailed in Section 3.2. We will revise the abstract to include a concise reference to these experimental validations and the adaptation process. revision: partial
Referee: [Method] Method description (flow-based propagation): the assumption that a network trained for motion completion in inpainting can extrapolate coherent motions in dynamic scenes and large expansion ratios lacks supporting analysis or targeted experiments demonstrating robustness to the lack of outer reference pixels, which is load-bearing for the superiority claim over diffusion baselines.

Authors: The manuscript supports this through extensive experiments in Section 4.1 and 4.3, where the method shows superior results over diffusion models specifically in dynamic scenes and large expansions, measured by metrics such as warping error and perceptual quality. The reference-guided latent propagation (Section 3.3) is designed to compensate for missing outer pixels by propagating from seen content across frames using the adapted flow fields. To strengthen the response to this valid point, we will add a targeted analysis subsection in the revision discussing robustness across expansion ratios and scene dynamics, including qualitative examples of motion extrapolation without outer references. revision: yes

Circularity Check

0 steps flagged

No circularity: method relies on external pre-training and standard fine-tuning without self-referential reductions.

full rationale

The abstract and described framework use a pre-trained flow completion network (from inpainting) that is then fine-tuned end-to-end. No equations, derivations, or load-bearing claims are presented that reduce by construction to parameters or definitions internal to the paper itself. The approach cites external pre-trained components and standard techniques; superiority claims rest on empirical results rather than any self-definitional or fitted-input-as-prediction structure. This matches the default expectation of a non-circular paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the method description mentions a pre-trained network and fine-tuning but does not enumerate any fitted constants or new postulated components.

pith-pipeline@v0.9.0 · 5513 in / 1125 out tokens · 40343 ms · 2026-05-10T12:08:27.154700+00:00 · methodology

discussion (0)

Reference graph

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