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arxiv: 2510.24718 · v3 · submitted 2025-10-28 · 💻 cs.CV · cs.LG

Generative View Stitching

Chonghyuk Song , Michal Stary , Boyuan Chen , George Kopanas , Vincent Sitzmann This is my paper

Pith reviewed 2026-05-18 02:49 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords video generationdiffusion modelscamera trajectorygenerative view stitchingtemporal consistencydiffusion forcingloop closing
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The pith

Generative View Stitching lets off-the-shelf video diffusion models follow any predefined camera path without collisions or drift.

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

Autoregressive video diffusion models receive only past frames as conditioning and therefore collide with the generated scene when forced to follow a future camera trajectory. Generative View Stitching instead samples the full sequence in parallel so every frame respects the entire predefined path. The method extends existing diffusion-stitching ideas to video but requires no retraining; it works on any model already trained with Diffusion Forcing. Omni Guidance adds bidirectional conditioning on both past and future frames, which stabilizes the output and enables explicit loop closing for long-range coherence. The result is frame-to-frame consistent video for paths that previously caused immediate failure, including closed loops such as the Impossible Staircase.

Core claim

Generative View Stitching samples the entire video sequence in parallel so the generated scene remains faithful to every segment of a user-specified camera trajectory. The algorithm extends prior diffusion-stitching techniques to video generation and requires no architectural changes or retraining when applied to models trained with Diffusion Forcing. Omni Guidance further conditions each frame on both past and future context, which improves temporal consistency and supports a loop-closing mechanism that maintains coherence over long horizons.

What carries the argument

Generative View Stitching, a parallel sampling procedure that stitches latent frames across the full camera trajectory, augmented by Omni Guidance for bidirectional past-and-future conditioning.

If this is right

  • Predefined camera paths of arbitrary complexity can be followed without the scene collapse typical of autoregressive rollouts.
  • Loop-closing becomes feasible, delivering long-range scene coherence that autoregressive models lose after the first collision.
  • Any existing video model trained with Diffusion Forcing can be used directly, removing the need for task-specific fine-tuning.
  • Frame-to-frame consistency is enforced by construction through the parallel stitching schedule.

Where Pith is reading between the lines

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

  • The same stitching schedule could be applied to other autoregressive generative domains where future constraints are known in advance.
  • Omni Guidance suggests a general pattern for adding future conditioning to any diffusion-based sequence model without architectural overhaul.
  • If the approach scales to higher resolutions, it could support real-time preview of camera moves inside generated environments.

Load-bearing premise

That models trained with Diffusion Forcing already contain the internal representations needed for stitching without any retraining or changes to the network.

What would settle it

Apply Generative View Stitching to a standard off-the-shelf Diffusion Forcing video model on the Impossible Staircase trajectory and check whether the output frames remain free of collisions and maintain visual consistency when the loop is closed.

Figures

Figures reproduced from arXiv: 2510.24718 by Boyuan Chen, Chonghyuk Song, George Kopanas, Michal Stary, Vincent Sitzmann.

Figure 1
Figure 1. Figure 1: Generative View Stitching (GVS) enables stable camera-guided generation of long videos. Given a pretrained DFoT video model (Song et al., 2025) with an 8-frame context window and predefined camera trajectory, GVS can generate a 120-frame navigation video that is stable, collision-free, faithful to the conditioning trajectory, consistent, and closes loops. On the other hand, Autoregressive sampling diverges… view at source ↗
Figure 2
Figure 2. Figure 2: Generative View Stitching (GVS) is a training-free diffusion stitching method that is compatible with any off-the-shelf video model trained with Diffusion Forcing (DF). We first partition the target video into non-overlapping chunks shorter than the model’s context window, then denoise every target chunk jointly with its neighboring chunks to condition on both the past and future. We use the denoised targe… view at source ↗
Figure 3
Figure 3. Figure 3: Effect of Omni Guidance and Stochasticity. Without Omni Guidance and zero stochastic￾ity (η = 0), the generations lack temporal consistency and instead exhibit hazy transitions between different scenes. Increasing stochasticity to its maximum (η = 1.0) enhances consistency but leads to oversmoothing. Our full method with Omni Guidance and partial stochasticity (η = 0.9) enables consistent generation withou… view at source ↗
Figure 4
Figure 4. Figure 4: GVS Requires Explicit Loop Closing. Despite its global theoretical receptive field, our stitching method requires an explicit loop closing mechanism to “visually return to the same place”. Note that the camera centers are offset from the panorama’s rotation center purely for visual clarity. Panorama [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Loop Closing via Cyclic Conditioning. GVS closes loops via cyclic conditioning, whereby target chunks are denoised by two alternating sets of context windows: temporal windows, which condition target chunks on their temporally neighboring chunks, and spatial windows, which condition target chunks on temporally distant but spatially close neighboring chunks. As a result, target chunks are conditioned on all… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative Comparison with Baselines. Autoregressive sampling collides with the generated scene, fails to dream up the desired staircase, and does last-minute loop closure, resulting in discontinuities in scene appearance. StochSync performs better at these tasks, but it generates shape-shifting scenes that lack temporal consistency. GVS, on the other hand, avoids collisions, generates the desired stairca… view at source ↗
Figure 7
Figure 7. Figure 7: GVS can visually navigate through the Impossible Staircase. GVS can generate a 120-frame navigation video through our variant of Oscar Reutersvard’s ¨ Impossible Staircase, which is shown on the left. The video forms a visually continuous loop between the end points of the conditioning camera trajectory despite their height difference. Guidance significantly improves long-range consistency, demonstrating t… view at source ↗
Figure 8
Figure 8. Figure 8: Spatial Context Windows that Define Our Method’s Cyclic Conditioning Strategy. Note that for Panorama 1-loop and Panorama 2-loop, we intentionally offset the camera centers from the panorama’s rotation center to better distinguish between different parts of conditioning trajectory. We visualize Circle 1-loop and Circle 2-loop as spirals also for better clarity. each other by 4 frames. While the default gui… view at source ↗
Figure 9
Figure 9. Figure 9: GVS Stably Scales to Longer Videos Given More Test-Time Compute. conditioned on a maximum of 3 retrieved history frames, which are placed at the right end of the context window, and the 4 latest history frames, which are placed at the left end; if there are less than 3 retrieved frames, we pad with pure-noise frames. We augment StochSync with our cyclic conditioning mechanism, which we tailor to its non￾ov… view at source ↗
Figure 10
Figure 10. Figure 10: Lowering Stochasticity Levels Reduces Oversmoothing. Note that we intentionally offset the camera centers from the panorama’s rotation center to better distinguish between different parts of the conditioning trajectory. Omni Guide η Panorama 2-loop F2FC(↓) LRC(↓) IQ(↑) AQ(↑) IS(↑) ✗ 0.9 0.166 0.133 0.402 0.355 1.59 ✗ 0.8 0.175 0.147 0.380 0.359 1.56 ✓ 0.9 0.155 0.116 0.483 0.376 2.03 ✓ 0.8 0.151 0.113 0.4… view at source ↗
Figure 11
Figure 11. Figure 11: GVS Struggles to Propagate External Context Frames Throughout the Entire Video. C LIMITATIONS AND CHALLENGES C.1 EXTERNAL IMAGE CONDITIONING We show that GVS does not effectively propagate externally provided context frames to the rest of the target video. In [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: This experiment suggests that this failure mode stems from the specific Diffusion-Forcing [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 12
Figure 12. Figure 12: GVS Fails to Loop-Close Wide-Baseline Viewpoints. Note that the camera centers of the forward and backward segments, which are collinear in implementation, are visually offset for better clarity. Forward-Orbit-Backward [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Diffusion-Forcing Backbone Fails on Wide-Baseline Camera Trajectories. Note that the camera centers, which are collinear in implementation, are visually offset for better clarity. frame 0 is absent in frame 13, which marks the end of the orbit, and the color of the floor in each frame is different. When our loop closing mechanism is activated, the forward and backward camera segments depict the same scene… view at source ↗
Figure 14
Figure 14. Figure 14: Additional Qualitative Comparisons with Baselines. Note that we intentionally offset the camera centers from the panorama’s rotation center to better distinguish between different parts of the conditioning trajectory. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: [Continued] Additional Qualitative Comparisons with Baselines. Note that we intentionally offset the camera centers from the panorama’s rotation center to better distinguish between different parts of the conditioning trajectory. We visualize Circle 1-loop and Circle 2-loop as spirals also for better clarity. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: [Continued] Additional Qualitative Comparisons with Baselines. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_16.png] view at source ↗
read the original abstract

Autoregressive video diffusion models are capable of long rollouts that are stable and consistent with history, but they are unable to guide the current generation with conditioning from the future. In camera-guided video generation with a predefined camera trajectory, this limitation leads to collisions with the generated scene, after which autoregression quickly collapses. To address this, we propose Generative View Stitching (GVS), which samples the entire sequence in parallel such that the generated scene is faithful to every part of the predefined camera trajectory. Our main contribution is a sampling algorithm that extends prior work on diffusion stitching for robot planning to video generation. While such stitching methods usually require a specially trained model, GVS is compatible with any off-the-shelf video model trained with Diffusion Forcing, a prevalent sequence diffusion framework that we show already provides the affordances necessary for stitching. We then introduce Omni Guidance, a technique that enhances the temporal consistency in stitching by conditioning on both the past and future, and that enables our proposed loop-closing mechanism for delivering long-range coherence. Overall, GVS achieves camera-guided video generation that is stable, collision-free, frame-to-frame consistent, and closes loops for a variety of predefined camera paths, including Oscar Reutersv\"ard's Impossible Staircase. Results are best viewed as videos at https://andrewsonga.github.io/gvs.

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

2 major / 2 minor

Summary. The paper introduces Generative View Stitching (GVS), a sampling algorithm that generates video sequences in parallel to ensure consistency with a predefined camera trajectory. It extends prior diffusion-stitching techniques to work with any off-the-shelf video diffusion model trained under the Diffusion Forcing framework, without requiring retraining or architectural changes, and adds Omni Guidance to improve temporal consistency via bidirectional conditioning and enable loop closure on complex paths such as the Impossible Staircase.

Significance. If the central claims hold, the work offers a practical advance in controllable video generation by leveraging existing Diffusion Forcing models for stable, collision-free, and loop-closed outputs under arbitrary camera paths. This could reduce the need for specialized training in applications like robotics simulation and immersive content creation, with the no-retraining compatibility representing a notable strength if supported by the experiments.

major comments (2)
  1. [Abstract and §3] Abstract and §3: The load-bearing claim that standard Diffusion Forcing models already supply the necessary affordances (future-frame conditioning and noise-schedule properties) for stitching without any retraining or modifications requires explicit verification. The manuscript should include a direct ablation or comparison demonstrating that unmodified DF rollouts support the parallel sampling and loop-closing behavior on paths like the Impossible Staircase; otherwise the extension from prior stitching work that typically needs specially trained models remains unproven.
  2. [§4.2] §4.2 (Omni Guidance): The description of bidirectional conditioning for temporal consistency and loop closure should specify the exact conditioning mechanism and noise schedule adjustments used during parallel sampling; without this, it is unclear whether the reported collision-free results rely on undisclosed model-specific tweaks.
minor comments (2)
  1. [Figures and Experiments] Figure captions and video links: Ensure all qualitative results include quantitative metrics (e.g., collision rate, consistency scores) alongside the visual examples for the Impossible Staircase and other paths.
  2. [Method] Notation: Define the precise form of the stitching loss or guidance term when extending diffusion stitching to video; the current description leaves the mathematical relationship to standard DF sampling implicit.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the practical value of Generative View Stitching for controllable video generation. We address each major comment below with point-by-point responses and indicate the revisions planned for the next version of the manuscript.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3: The load-bearing claim that standard Diffusion Forcing models already supply the necessary affordances (future-frame conditioning and noise-schedule properties) for stitching without any retraining or modifications requires explicit verification. The manuscript should include a direct ablation or comparison demonstrating that unmodified DF rollouts support the parallel sampling and loop-closing behavior on paths like the Impossible Staircase; otherwise the extension from prior stitching work that typically needs specially trained models remains unproven.

    Authors: We appreciate the referee's request for stronger empirical grounding of this central claim. Section 3 of the manuscript already explains that Diffusion Forcing training produces per-frame noise schedules and supports conditioning on both past and future frames within the same rollout, which directly enables the parallel stitching procedure without architectural changes. To provide the requested explicit verification, we will add a new ablation subsection (Section 4.3) in the revised manuscript. This ablation will compare unmodified DF autoregressive rollouts against our parallel GVS sampling on the Impossible Staircase trajectory, demonstrating that the DF affordances suffice for collision-free loop closure while standard autoregression collapses. We believe this addition will clearly distinguish GVS from prior stitching methods that require specialized training. revision: yes

  2. Referee: [§4.2] §4.2 (Omni Guidance): The description of bidirectional conditioning for temporal consistency and loop closure should specify the exact conditioning mechanism and noise schedule adjustments used during parallel sampling; without this, it is unclear whether the reported collision-free results rely on undisclosed model-specific tweaks.

    Authors: We agree that greater specificity is needed for reproducibility. In the revised Section 4.2 we will expand the description of Omni Guidance to state the exact mechanism: during parallel sampling, each frame's noisy latent is concatenated with the noisy latents of both preceding and succeeding frames in the input context, and a uniform noise level is applied across the entire sequence (rather than the per-frame schedule used in standard DF inference). This bidirectional conditioning is implemented via the standard Diffusion Forcing forward pass with no additional model-specific modifications or retraining. We will also include pseudocode for the sampling loop to make the procedure fully transparent. revision: yes

Circularity Check

0 steps flagged

No significant circularity; algorithmic extension is self-contained

full rationale

The paper's core contribution is a sampling algorithm extending prior diffusion-stitching methods to video generation. It claims compatibility with off-the-shelf Diffusion Forcing models by demonstrating that the framework supplies the required conditioning affordances for parallel sampling and loop closure, without retraining or architectural changes. This is presented as an empirical and algorithmic observation rather than a self-referential definition, fitted parameter renamed as prediction, or load-bearing self-citation chain. No equations or steps in the provided abstract reduce the result to its inputs by construction; the derivation remains independent of the target outcomes and relies on external model properties shown through application.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The approach relies on the properties of Diffusion Forcing training but introduces no new free parameters, axioms, or invented entities beyond the sampling algorithm itself.

pith-pipeline@v0.9.0 · 5771 in / 1036 out tokens · 19390 ms · 2026-05-18T02:49:51.885022+00:00 · methodology

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Reference graph

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    In other words, each 8-frame-long context window is comprised of 2 frames from the past chunk, the target chunk, and 2 frames from the future chunk. We visualize this in Fig. 2 but with half the context window size. Loop-Closing Mechanism. For conditioning camera trajectories that require loop-closing i.e., Panorama 1-loop , Panorama 2-loop , Circle 1-loo...

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