Generative View Stitching
Authors: Chonghyuk Song
Michal Stary
Boyuan Chen
George Kopanas
Vincent Sitzmann
Venue: ICLR 2026 Poster (2026)
Keywords: Long Video Generation
Camera-guided Video Generation
Video Diffusion Models
Primary area: generative models
TL;DR: A non-autoregressive alternative to length extrapolation of video diffusion models, which enables collision-free video generation for predefined camera trajectories.

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 Reutersvärd’s Impossible Staircase.

Published as a conference paper at ICLR 2026
GENERATIVE VIEW STITCHING
Chonghyuk Song' Michal Stary'* Boyuan Chen' George Kopanas Vincent Sitzmann
'MIT CSAIL "Runway ML
ABSTRACT
Autoregressive video diffusion models capable of long rollouts that 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 pre- defined camera trajectory, this limitation leads to collisions with the generated scene, after which autoregression quickly collapses. To address this, we propose Generutive View Stitching (GVS), which samples the entire sequence in purallel such that the generated scene is faithful to every part of the predefined camera trajectory. Our main contribution is sampling algorithm that extends prior work on diffusion stitching for robot planning to video generation. While such stitching methods usually require specially trained model, GVS is compatible with any off-the-shelf video model trained with Diffusion Forcing, a prevalent sequence diffusion framework that show already provides the affordances necessary for stitching. We then introduce Omni Guidance, a technique that enhances the tempo- ral 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- camera-guided video generation that is stable, collision-free Crame-to-frame consistent, and closes loops for a variety of predefined camera paths, including Oscar Reutersvärd's Impossible Staircase. Results are best viewed as videos at /
gew..ggitti. github. io/gvs/
INTRODUCTION
Recent developments in diffusion models have significantly advanced video synthesis. However, current video diffusion models generate videos with limited context: most models (Kong al.)
Luma AT Team 2024| Google DeepMind 2025; Wan et al., 2025) Runway 2025) generate videos between 5 and 10 seconds. This is often less than what users want generate. Because training models with longer context is costly, methods that can extrapolate pretrained models to generate videos longer than the training context length have become an attractive prospect. Existing approaches typically involve "rolling out" short-horizon model autoregressively; they demonstrate temporal coherence and stability over the course hundreds of frames (Chen et al., 2024 Song et al., 2025) and enable real-time streaming (Yin et al. 2025 Huang et al. 2025). Autoregressive (AR) sampling can also be extended with retrie techniques to enable consistency with respect to
Luma
the distant past (Zhou et al. 2025 Xiao et al. 2025: Yu et al. [2025] Cai et al. 2025). paving the way for neural game engines and interactive world models (Valevski et al. 2025 Ball et al. 2025).
However, AR sampling does not enable consistency with respect to the future, i.e., beyond the current context window: it is not possible to guide the generation to synthesize a future goal frame or for current generations to be consistent with future conditioning variables. This arises in offlie applications that require high-level planning, such as one-shot cinematography (Failes) 2020 Yates] 2023) and synthetic scenario generation for autonomous driving (Hu al. 2023 Russell et al. 2025). which entail camera-guides video generation with predefined camera trajectory. In this task setting, AR sampling may generate a wall that it later forced to "step through" due to its inability to plan ahead. This results in exposure bias (Schmidi 2019 Huang et al. (025). where the generated video frames become out-of -istr---istiiuution to the model and autoregression quickly collapses (see Fig. T.
To address this, we explore nuntauutoreeeessivv sampling approach to video length extrapolation, which respects future conditioning signals when generating current frames. We are inspired by
Work done visiting student MIT.

---

Published as a conference paper at ICLR 2026
Generative View Stitching
Autoregressive Sampling staircase
loop closure staircase stable long rollout
late loop closure
diverges
Staircase Circuit
Figure 1: Generative View Stitching (GVS) enables stable camera-guided generation of long videos. Given pretrained DFoT video model (Song et al., 2025) with an 8-frame context window and predefined camera trajectory, GVS can generate 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 due collisions with the generated scene, conditioning trajectory, and demonstrates poor loop closure even when augmented with RAG.
faithful to the
previous work in diffusion stitching (Liu et al. 2022 Bar- Tal et al., 2023 Mishra et al. 2023
Kim et al. 2024; Goli et al. 2025). which are sampling methods that generate the entire sequence in parallel, by dividing the sequence into overlapping segments and intertwining their generation processes for consistent connections. Despite their potential for video length extrapolation, existing stitching methods not suitable for video generation. For example, StochSyne Yeoo et al.) 2025). originally designed for generating images such as 360-degree panoramas and 3D mesh textures, lacks the temporal consistency necessary for video generation,
later show in Sec. 4. Com- PDiffuser (Luo et al. (2025) requires a sequence diffusion model specially trained for stitching, but training such custom model for video costly.
Based on these observations, we propose Generative View Stitching (GVS), the first stitching method for camera- camera-guidee guided video generation. GVS is a training-free stitching approach that is designed be compatible with any off-the-shet video model trained with Diffusion Forcing (Chen et al.) 2024), prevalent framework
training sequence diffusion models (Decart et al. 2024 Yin et al. 2025 Song et al. 2025 Chen et al. 2025a Sand.ai et al. 2025). We then introduce Omni Guidance, which enhances temporal consistency stitching by strengthening the conditioning on the past and future and, in turn, enables proposed loop-closing mechanism for long-ran range consistency. Our stitching method achieves long- song-ooizon camera-guided video generation that is stable, collision-fre and consistent across both short- and long long-term time horizons for variety of predefined camera paths, including Oscar Reutersvärd's Impossible Staircase (Penrose & Penrose 1958) (see Fig.
RELATED WORK AND PRELIMINARIES
Diffusion models. Diffusion models (Sohl- Sicl-DDcksttinn et al. 2015 Ho et al. 2020: Song et al. 2021b generative models defined by forward process that iteratively corrupts data sample from target distribution into white noise through a series noising steps x Va vI atc where k (0, 1, ..... K 1} are increasing noise levels, {a* is a predefined schedule, and (0, I). The goal is to reverse the forward process by learning the score function eg(x*, k)), which enables iterative denoising of white noise into sample from the target distribution via:
V
x²
(x Vak
V a - (ak) cg ()),,,)))) tate (1)
Song al.
a), where t controls the level of stochasticity iee., the amount of random noise injected into each denoising step, concept that will become important later on.
NO
Long Video Generation. Video diffusion models often fall short in terms of temporal resolution. resulting in videos only 5 to 10 seconds long (Kong et al, 20024) Luma AI Team 2024] Google Runway 2025). This is because the typical diffusion model
DeepMind 2025
Wan et al. 2025
2

---

Published as a conference paper at ICLR 2026
2023) uses attention layers that scale quadratically with the number of tokens. One way to avoid this is to retrieve and attend only to a select number of tokens that are relevant for generating each frame (Xiao et al. 2025 Yu al. [025 Cai et al. 2025). Alternatively, history can be compressed into a hidden state (Dalal et al., 2025; Zhang et al. 2025). Training
architecture (Peebles & Xie
models with large context is an exciting direction, and test-time stitching methods like GVS can piggy-back and extrapolate to even longer sequences by using such backbone models. This highlights an important property of GVS, which modifies only the sampling method and does not require specialized model architectures or training paradigms.
Diffusion Forcing and Autoregressive Extension. Diffusion Forcing (DF) (Chen et al. 2024) is a
prevalent framework for training sequence diffusion models (Decart et al.] 2024 Yin et al. [2025] Song et al. 2025; Chen et al. 2025a Sand.ai et al., 2025). DF trains sequence diffusion models with independent noise levels per token. During sampling, DF models can then selectively mask portions of their context window with noise, enabling conditioning on a variable number of context tokens, also referred to as history. DF also gives rise to history guidance (Song et al.) 2025), which enables ultra-long camera-guided autoregressive video generation that is stable and consistent. However, the long rollouts presented in these works are only possible because of real-time, user-controlled camera trajectories that prevent collisions with generated scene elements. Autoregressive sampling according to predefined camera trajectory leads collisions of the camera with the generated scene as the model generates content unaware of the future camera trajectory (see Fig. and Fig. G6- We present a stitching- basedd alternative that (1) compatible with any DF model and (2) generates videos that are faithful to the full camera trajectory and are thus collision-free and stable.
Ganera-Guided Video Diffusion. Camera control is highly desired feature of video diffusion models due to its far-reaching applications in cinematography, game development, and world simula-
tion. Recent approaches to camera control typically involve adding camera conditioning to pretrained
T2I or T2V diffusion models (Wang et al. [023] Guo et al. 2024] Wang et al., 2024] He et al.. 2025a
Bahmani et al. 2025b a. Bai et al. 2025] et al. 2025b Zhou et al. 2025) and finetuning them on
pose-annotated videos. Some prior works train conditondt video models from scratch (Song ct al. 2025 Weber et al. 2026). To generate videos longer than the context window of these backbone models, many works use autoregressive (AR) extension
sampling-time solution (Song et al., 2025 Zhou et al. 2025 Schneider et al., 2025 Xiao et al. 2025). However, AR sampling can lead to collisions with the generated scene, due to its inability to look at future camera poses. Some works circumvent this issue either by having the user control the camera trajectory in real-timo time (Song et al.) 2025) or using a 3D-prior-based online planner for collision avoidance (Schneider al., 2025), but online methods are not suitable for video generation with respect to a predefined camera trajectory, which requires high-level planning. Our method is designed for such offline problem settings. which arise in one-shot cinematography and synthetic data generation for autonomous driving.
3 GENERATIVE VIEW STITCHING
We motivate and describe the key components of Generative View Stitching (GVS), diffusion stitching method for camera-guides -uidddd video generation that overcomes the shortcomings of autore- gressive sampling. First, we review recent stitching method CompDiffuser (Luo et al. 2025) and its requirement of a custom-trained model (Sec. 3.1 We then make the key observation that any video model trained with Diffusion Forcing (DF) already has the necessary affordances support stitching, and introduce a corresponding training stitching method (Sec. We overcome issues with temporal consistency by combining maximum stochasticity (Yeo et al.) 2025). which itself is insufficient (Sec. 3.3). with our novel Omni Guidance, which enhances temporal consistency by strengthening the conditioning on the past and future (Sec. (3.4). Omni Guidance further enables
(S44.
our loop closing mechanism for delivering long-range coherence (Sec. 3.5
3.1 CHALLENGES IN EXTENDING DIFFUSION STITCHING TO VIDEO GENERATION
Diffusion stitching methods (Lu et al. 2022 Bar- Tal et al. 2023 Mishra et al. 2023 Kim et al. 2024
Yeo et al. 2025 Goli et al. 2025 sampling methods that enable compositional general lization beyond the context window. These methods typically divide the target sequence x into T overlapping chunks {x, ) and intertwine their denoising processes by synchronizing their intermediate outputs. In particular, CompDiffuser (Luo et al. 2025), a stitching method for coal-conditioned planning,
3

---

Published as a conference paper at ICLR 2026
Noise level
Future
Conditioning
Conditioning
OF mode
colx²,k)
DF model
Discard
Discard
Noise level k-1
Figure 2: Generative View Stitching (GVS) is vaining-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 's context window, then denoise every target chunk jointly with its neighboring chunks to condition on both the past and future. We the denoised target chunk of every context window update the noisy stitched sequence while discarding the denoised past and future conditioning chunks. We further enable Omni Guidance (Sec. 3.4), which enhances temporal consistency, by replacing the original score function eg with the guided score function ég in Eq. g
models each target chunk x, to be dependent only on its temporal neighbors X X resulting in the following compositional trajectory distribution:
T-2 Pe 11111111111111
po (x Xatars X Po(oo) Xstart, X x Xgoal
II
(2)
where Nstatt and Xgoal denote the predefined start and goal states, respectively. However, to realize this, CompDiffuser must train customized denoising network <n +|x{_1xx++1 that generates target chunk conditioned on its co-evolving, noisy neighboring chunks. These conditioning chunks treated as separate conditioning inputs, which are embedded via special encoder and then injected to the backbone model via Adaptive LayerNorm. This need for a custom model prevents the application of CompDiffuser to off-the-shelf models, and hence makes it infeasible use for video where training a custom model would incur an unacceptable cost.
3.2 GENERAL-PURPON VIDEO MODELS ALREADY ENABLE STITCHING
We seek to design training-fres stitching method that compatible with off-the-shelf video models. We find that remarkably, a widely used training framework, Diffusion Forcing (DF), already provides all the necessary features for stitching. Like CompDiffuser, represent the distribution camera- guided videos x compositionally:
T-1
(x(x & II t
p. (x Pt-1. P(+1),
where is the predefined camera trajectory, p-1 Po and PT PT-1, and and x7 pure-noise frames for padding. Unless otherwise stated, we omit the camera trajectory for the sake of brevity. CompDiffuser requires custom backbone model for stitching it processes neighboring chunks via a specialized conditioning path. We instead propose to condition the target chunk x on its temporal neighbors x xt_ by jointly denoising them part of the same input sequence the
4

---

Published as a conference paper at ICLR 2026
Straight Line
Stairs
Time
Time
(vanilla CVS)
OmmiCuide No OmniGadk No OmniCuide
a= 00
(fall cvS)
0.3
Figure 3: Effect of Omni Guidance and Stochasticity, Without Omni Guidance and zero stochastic- ity (1) 0), the generations lack temporal consistency and instead exhibit hazy transitions between different scenes. Increasing stochasticity to its maximum (1 = 1.0) enhances consistency but leads oversmoothing. Our full method with Omni Guidance and partial stochasticity (17 0.9) enables consistent generation without oversmoothing.
model [x(_ x" x x++1) shown in Fig. We partition the target video x into T non-overlapping chunks (x. 12'-1 that are shorter than the context window, thereby freeing up space to jointly denoise the conditioning chunks. In practice, only a portion of the neighboring chunks fit into the context window. In the model output, the denoised target chunk x is used to update the noisy estimate k-1
of the stitched sequence while the denoised conditioning chunks x Xt+1 are discarded. This stitching procedure, which we refer to as vanilla GVS, is compatible with any DF video model, which is designed for such joint denoising of conditioning and target signals (Song al. 2025).
Although vanilla GVS is simple to implement, it achieves poor temporal consistency, as shown in the top of Fig. 3 We hypothesize that this is because vanilla GVS denoises the target chunk x using the score function of the joint distribution p(x,-1- 1- Xt, Xx+1) rather than that of the originally intended conditional distribution p(x, X Xx++) ) in Eq B While in autoregressive sampling, target
frames are generally conditioned on past context that is significantly less noisy, vanilla GVS requires that the target chunk x is equally noisy its conditioning neighbors x X leading weak conditioning signal.
3.3 STOCHASTICITY IS NECESSARY BUT INSUFFICIENT FOR CONSISTENCY
Prior stitching work StochSync (Yeo et al. 2025 proposes stochasticity as mechanism for enhancing consistency. introduces maxinum stochasticity o² VI --1 which acts as an error correction mechanism by climinating the predicted noise term and maximizing the random noise term in the denoising equation in Eq. [ We find that maximum stochasticity also benefits vanilla GVS in terms of temporal consistency (see Table 2 but often results in oversmoothed generations, as shown in
Fig. 3 aligning with observations made in prior works (Karras al. 2022 Yeo et al. 2025). this case, maximum stochasticity simplifies the task of consistency by oversmoothing the generations.
3.4 OMNI GUIDANCE ENHANCES CONSISTENCY
To address the shortcomings of stochasticity, we propose more direct way enhancing consistency: Omni Guidance, which aims to steer the score function of the original joint distribution towards that of the desired conditional distribution by strengthening the conditioning on the past and future.
One difficulty is that stitching breaks Salimans| 2022). which that the conditioning signal is independent of the model weights. GVS, the guidance signal for target chunk x comes from the backbone model's co-evolving, noisy
assumption of standard classifier-fre guidance (Ho &
5

---

Published as a conference paper at ICLR 2026
Panorama 1-loop
Panorama 2-loop
Time
Time
Loop dlosin
disabled
Loop deeinn enabled
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 offset from the panorama's rotation center purely for visual clarity.
estimate of its temporal neighbors x xf+1, making the guidance signal dependent on the model weights. To address this, we draw inspiration from Inner Guidance (Chefer et al. (025) and directly modify the original sampling distribution po -x 1 11--1:+1)] for target chunk x, to be consistent with its temporal neighbors (and the predefined camera trajectory):
Po 1
(4)
px Pr. 1: (P: (P xxt 111+11111 124
Px(xx x |x, Po-1:441 7
(5)
po px 1 Pr- 1:141] 17
pe (x+ 1:t (Pt-
a po 1 (P-11:4)))
(6)
Po (x# (1111)
(x Pt-1:141)
This corresponds modifying the original score function eo ( 1++1 Pr-1:(+) follows:
Ep = (1 (1+7) + 72)c0 { (x Pe-
10,0,0 7200 x 0p:-1a41), (7)
where @ denotes the null condition and guidance scales " and 7/ modulate the adherence to the prede- fined camera trajectory and consistency of the target chunk with its temporal neighbors, respectively. In practice, merge the guidance terms be modulated by single obtain:
Es (1 + 7)e (x* 1
(0, x 010, 0,0).
(8)
The guidance term (0, x 010. 0, 0) is computed by replacing the noisy neighboring chunks with pure Gaussian noise and setting their noise levels to be maximum, enabled by relying on the Diffusion Forcing backbone (Chen et al. (024). This can be seen generalization of Fractional History Guidance (Song et al. 2025). with the key difference that the noise levels of the conditioning neighboring chunks, change throughout the stitching process while they remain fixed in history- guided autoregressive sampling.
As we show in Sec. 4.2) Omni Guidance enhances temporal consistency across a wide range of stochasticity levels and thus provides extra affordance for reducing oversmoothing, Specifically, Omni Guidance enables partial stochasticity of r/v1 --1 where 10, the two of which tandem reduce oversmoothing while maintaining similar levels of consistency.
3.5
RANG-RANGE CONSISTENCY VIA CYCLIC CONDITIONING
In theory, our stitching method described in Sec. 3.2 through 3.4 has global context as the theoretical receptive field each segment grows with every denoising step, somewhat analogous to the growing receptive field along the depth of CNN (Luo al. 2017). Therefore, one could reasonably expect GVS to enable zero-shot loop closure, a form of global consistency. GVS significantly improves temporal consistency, it does not enforce it globally. Fig. demonsttaaes that very long generations do not visually "return to the same place". suggesting that information does not propagate as widely across the stitched video necessary.
practice, we observe that while

---

Published as a conference paper at ICLR 2026
Panorama
Panorama
1-loop
2-loop
aed
Temporal Window
Spatial Window
Temporal Window
Spatial Window
Figure 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 temporally distant but spatially close neighboring chunks. As a result, target chunks are conditioned on all relevant neighbors across the entire stitching process. See Fig. for the full set of spatial windows.
To enable loop closures, we propose adding more factors to the compositional distribution in Eq, by denoising additional diffusion windows that contain temporally distant but spatially close chunks jointly with the original set of diffusion windows. For example, shown in Fig. S the chunk containing the frame at the end of the Panorama 1-1oop trajectory is denoised by two diffusion windows: one that conditions the target chunk its temporal neighbors (temporal window) and one on its spatial neighbors (spatial window). We propose to alternate between these two sets of context windows at every denoising step, a procedure we call cyclic conditioning. As result, the generation of the target chunk is conditioned on both of its spatial and temporal neighbors over the course of the entire denoising process, resulting in successful loop closure. We visualize the full set of spatial windows for each trajectory in Fig. 8 Note that some target chunks do not have spatial neighbors and are denoised only by temporal windows.
4 EXPERIMENTS
We empirically evaluate GVS as stitching method for camera-guie gurided video generation and as an alternative to autoregressive sampling for video length extrapolation. Further experimental details and results can be found in Appendix and B and video results on our purppooecct page
Benchmarks. To evaluate long-horizon, camera-guided video generation against predefined camera trajectory, we create a dataset of challenging conditioning trajectories, which are listed in Table These camera trajectories are designed to test various video model capabilities, including video length extrapolation, loop closures, and collision avoidance.
Baselines. 1) History- Guided Autoregressive (AR) Sampling (Song et al. 2025 autoregressive extension method for ultra-long video generation. 2) StochSync (Yeo et al. 2025): a diffusion stitching method for panorama generation and 3D mesh texturing. All sampling methods, including ours, are evaluated using the same camera-conditionel video model open-sourced by [Song et al. (2025). Diffusion-Foreing Transformer model trained on the RealEstate 10K dataset (Zhou et al.) 2018) with an 8-frame-long context window. For conditioning trajectories that require loop closing, augment Autoregressive Sampling with memory mechanism built on feld-of-view-bbase retrieval (Zhou et al.) 2025 Xiao et al. 2025 and StochSyne with our proposed loop-closing mechanism.
Metrics. To evaluate the trame-to-frane consistency (F2FC) of camera-guided vidco generation, we use MEt3R cosine (Asim et al., (025) averaged over every pair of consecutive frames. We measure long-range consistency (LRC) also with ME13R cosine (Asim et al., 2025). now averaged over pairs of frames that are temporally distant but deemed to be spatially close based on the field-of-view overlap of their conditioning cameras. We use the collision detection mechanism in (Schneider et al.) 2025) to evaluate collision avoidance (CA), whereby a collision is claimed if the inferred metric video depth for any frame (Chen et al. 2025b) falls below threshold. Finally, evaluate video frame quality using the imaging quality (IQ) and aesthetic quality (AQ) metric proposed in VBench (Huang et al. 2024) and we use the inception score (IS) for certain ablations (Salimans et al. 2016).
7

---

Published as a conference paper at ICLR 2026
Autoregressive
StochSync
Ours (GVS)
Trajectory
FIRO()) (RC()) 1Q(1) AQTT CAQ) PIPOL) LRC()) 1Q(1) AQXT) CA(L) FIFC()) LRO() 1Q11> AQX1) CAC))
Panorama
loog
0.108
0.339 0.458
0.409
NAA
0.183
0.164 0.515
0.489
N/A 0.138 0.141 0.587 0.461
N/A
Panorama 2-1oop
0.169
0.171
0.460
0.422
N/A
0.259
0.279
0.500
0.470
N/A 0. 0.555
0.118 0.483 0.376
NIA
Circle Loop
0.220
0.411
0.482
0.377
d.025
0:204
0.268
0.546
0.459
0.160
0.244
0.546
0.432
Circle 2-1cop
0.207
0.280
0.459
0.387
0.775
0.252 0.305
0.419
0.182
2-208
0-465
0.358
Streight
0.166 NiA
0.417
0.137
0.401
Staircase circuit
0.132 0.449 0.397 0.329 0.625 0.179 0.221 0.583
0.438 0.129 0.176 0.607 0.419
Table I: Comparison with Baselines on Camera-guided Video Generation. Our method outper- forms both baselines in terms of temporal consistency (F2FC), long-range consistency (LRC), and collision avoidance (CA), while demonstrating comparable video quality (IQ, AQ). Note that while StochSync has zero collisions on paper, it achieves this by shape-shiftig the scene, as reflected in its poor temporal consistency. We display results averaged over 40 generations.
Straight Line
Stairs
geseed coRe
Panorama
2-loop
Time
Time
Time
Stoch Sync Autoreg..
GVS
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. StochSyne 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 staircase, and closes loops, all the while maintaining temporal consistency.
4.1 COMPARISON WITH BASELINES
As shown in Table 1 method outperforms both baselines in terms of temporal (frame-to-frame) consistency, long-range (loop) consistency, and collision avoidance, while demonstrating comparable video generation quality. These quantitative results are corroborated in Fig. 6 which demonstrates AR sampling's 's inability to plan and take future conditioning into account: on the St Straight Line benchmark, AR sampling collides with the generated scene, after which generations quickly collapse (see frames in green). More notably, on Stairs, AR sampling often fails to dream up the desired staircase and instead collides with the ceiling. On Panorama 2- 2-1oop, AR sampling demonstrates loop closing abilities (compare frames in purple) but often loop-closes at the "last-minute together visually inconsistent scenes to return to the same place (compare frames in red). StochSync avoids collisions and generates the desired staircase Straight Line and Stairs, but it does so by shape-shie shifting the scene and compromising on temporal consistency. On P Panorane ama 2-1oop, StochSync also demonstrates loop closing, but often fails reconcile high-frequency details, which
stitching
8

---

Published as a conference paper at ICLR 2026
Straight line
Stairs
Straight 1ine
Stairs
F2FCU))
101t)
AQrt)
15(t)
FarC))
10xt)
AQ())
15(t)
PIFCL)
10(t)
NoXt>
FIFOL)
100t)
18tt)
0.153
0.587
0.420
2.17
0.201
0.650
0.392
1.81
0.138
0.553
0.453
2.43
0.177
0.088
0.409
2.10
0.5 0.124
0.490
0.407
1.70
0.177
0.540
0.397
1.48
0.5
0.110
0.556
0.468
2.06
0.360
0.578
0.419
1.72
0.9
0.064
0.458
0.400
1.40
0.146
0.539
0.381
1.51
0.9
0.080
0.423
0.621
1.66
1.0
0.061
0.422
0.407
1.54
0.136
0.500
0.396
1.40
1.0
0.071
0.610
0.431
1.63
0.130
0.600
0.404
1.67
(a) without Omni Guidance
(b) with Omni Guidance
Table 2: Ablation on Omni Guidance and Stochasticity. Without Omni Guidance (a), increasing stochasticity consistently improves temporal consistency (F2FC) but often results in oversmoothing, which is reflected in the general decline of video quality metrics (IQ, AQ, IS), Omni Guidance (b) complements stochasticity by enhancing consistency across wide range of stochasticity levels. providing our method extra affordance to reduce oversmoothing.
Loop Closing
Omni Guidance
Panorama 1 1-1oop
Panorama 2-1oop
F2
(RC())
1Q(t)
AQX1)
P2FC()
LRC()
1Q(1)
AQ(T)
x
x
0.137
0.950
0.442
0.423
0.137
0.917
0.442
0.414
/
0.141
0.962
0.554
0.463
0.140
0.917
0.546
0.461
V
x
0.138
0.430
0.407
0.133
0.402
0.356
0.138
0.141
0.537
0.461
0.155
0.116
0.483
0.376
Table 3: Ablation on Loop Closing and Omni Guidance. Without an explicit loop closing mechanism, our method fails to display long-range consistency (LRC), even with the help of Omni Guidance. Activating our loop closing mechanism significantly improves long-range consistency, which can be further bolstered by Omni Guidance.
reflected in Table D GVS, on the other hand, generates samples that are stable, faithful to the conditioning camera trajectory (i.e., avoids collisions and generates the desired staircase), temporally consistent, and visually close loops. Please find more baseline comparisons Fig. 18 19 and 20
4.2 ABLATIONS
Omni Guidance and Stochasticity. In Table 2 and Fig. 3 we demonstrate the effectiveness of
Omni Guidance and how it complements stochasticity. Table 2 (a) shows that increasing stochasticity consistently improves temporal consistency. However, this comes at the cost of oversmoothed generations, as shown by the second row of Fig. 3 and the general decline in video quality metrics in
Table 2 (a).
In (b), Omni Guidance enhances temporal consistency across a wide range of stochasticity levels, providing GVS additional flexibility to reduce oversmoothing; going from maximum stochas- ticity (17 = 1) to partial stochasticity (0 17 1) reduces oversmoothing but worsens consistency, which can be compensated for by adding Omni Guidance (compare rows 2 and 3 in Fig. and (a) and row of Table 2 (b). respectively).
compare row 4 of Table 2
Note that for Straight Line adding Omni Guidance at maximum stochasticity 1 actually hurts temporal consistency, on paper. This is because oversmoothing is so severe that adding Omni Guidance, which generally increases scene complexity, makes the task of consistency a harder one.
Loop Closure and Omni Guidance. Table and Fig. 4 we highlight the need for an explicit loop closing mechanism in stitching and illustrate how Omni Guidance bolsters loop closing. As shown in Fig. without our proposed loop closing mechanism our method fails to visually return to the same place, which suggests that the effective receptive field of GVS is not global. This finding is corroborated in Table 3], which shows that even Omni Guidance cannot make up for the absence of explicit loop closing. When our loop closing mechanism is activated, however, adding Omni Guidance significantly improves long-range consistency, demonstrating that both components of our method are crucial for effective loop closure.

---

Published as a conference paper at ICLR 2026
visual loop closure
staircase
stable long rollout
Top View Side View
Figure 7: GVS can visually navigate through the Impossible Staircase. GVS can generate 120-frame navigation video through our variant Oscar Reutersvärd's Impossible Staircase, which is shown on the left. The video forms visually continuous loop between the end points of the conditioning camera trajectory despite their height difference.
It should be noted that activating our loop closing mechanism results in oversmoothed generations on Panorama 2-1oop loo for the default stochasticity level = 0.9 (see Fig. a)) However, we be further reduced to alleviate oversmoothing without compromising consistency, which is made possible by Omni Guidance.
show in Appendix [B.3] that stochasticity
4.3 NEW APPLICATION: THE IMPOSSIBLE STAIRCASE
In Fig. showcase novel application that leverages all of this paper's contributions: generate video that navigates through a variant of Oscar Reutersvärd's impossible staircase (Penrose & Penrose 1958). The two end points of the conditioning trajectory differ in height, yet form visually continuous loop by using our proposed loop-closing mechanism. Please find the implementation details in Appendix A.2
DISCUSSION
Linitations. GVS achieves collision-free samera-guided video generation by breaking causality, the downside of which is that it cannot be applied to online problem settings where future camera poses are not available, such as interactive video generation and streaming. Furthermore, we find that conditioning the rest of the target video. GVS does not require retraining of the backbone, which also means that GVS' performance is dependent viewpoints, which are not its backbone's 's training data, and often struggles to distinguish the start of upward staircase from the end of a downward staircase due the backbone's limited context window (see Fig. [].. Finally, the spatial windows in our cyclic conditioning strategy are manually defined and hence not currently scalable. We elaborate these shortcomings and suggest future work in Appendix
external images is difficult as GVS struggles to propagate the context frames to
its backbone. For example, GVS fails to loop-close wide-baseline
Conclusion. In this paper, we introduced Generative View Stitching (GVS), training-free free diffusion stitching method for camera-guide video generation. GVS is designed to be compatible with any Diffusion-Forrts video model, which enables Onni Guidance, a robust technique that enhances the temporal consistency in stitching, and by extension, enables loop closing. Given its ability to generate camera-ga guided videos that are consistent, faithful to the conditioning trajectory, and stable, GVS not only establishes itself as competitive video stitching framework, but also presents promising alternative to autoregressive extension for long video generation.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Mohammad Asim, Christopher Wewer, Thomas Wimmer, Bernt Schiele, and Jan Eric Lenssen. Met3r: Measuring multi-view consistency in generated images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
Sherwin Bahmani, Ivan Skorokhodov, Guocheng Qian, Aliaksandr Siarohin, Willi Menapace, Andrea Tagliasacchi, David B. Lindell, and Sergey Tulyakov. Ac3d: Analyzing and improving 3d camera control in video diffusion transformers. In IEEE Conference on Computer Vision and Puttern Recognition (CVPR), 2025a.
Sherwin Bahmani, Ivan Skorokhodov, Aliaksandr Siarohin, Willi Menapace, Guocheng Qian, Michael Vasilkovsky. Hsin- Ying Lee, Chaoyang Wang. Jiaxu Zou, Andrea Tagliasacchi, David B. Lindell, and Sergey Tulyakov. Vd3d: Taming large video diffusion transformers for 3d camera control. In International Conference on Learning Representations (ICLR), 2025b.
Jianhong Bai, Menghan Xia, Xiao Fu, Xintao Wang, Lianrui Mu, Jinwen Cao, Zuozhu Liu, Haoji Hu, Xiang Bai, Pengfei Wan, et al. Recammaster: Camera-controlled generative rendering from single video. In IEÉE International Conference on Computer Vision (ICCV), 2025.
Philip J. Ball, Jakob Bauer, Frank Belletti, Bethanie Brownfield, Ariel Ephrat, Shlomi Fruchter, Agrim Gupta, Kristian Holsheimer, Aleksander Holynski, Jiri Hron, Christos Kaplanis, Marjorie Limont, Matt McGill, Yanko Oliveira, Jack Parker-Holder, Frank Perbet, Guy Scully, Jeremy Shar, Stephen Spencer, Omer Tov, Ruben Villegas, Emma Wang. Jessica Yung, Cip Baetu, Jordi Berbel, David Bridson, Jake Bruce, Gavin Buttimore, Sarah Chakera, Bilva Chandra, Paul Collins, Alex Cullum, Bogdan Damoc, Vibha Dasagi, Maxime Gazeau, Charles Gbadamosi, Woohyun Han, Ed Hirst, Ashyana Kachra, Lucie Kerley, Kristian Kjems, Eva Knoepfel, Vika Koriakin, Jessica Lo, Cong Lu, Zeb Mchring, Alex Moufarek, Henna Nandwani, Valeria Oliveira, Fabio Pardo, Jane Park, Andrew Pierson, Ben Poole, Helen Ran, Tim Salimans, Manuel Sanchez, Igor Saprykin, Amy Shen, Sailesh Sidhwani, Duncan Smith, Joe Stanton, Hamish Tomlinson, Dimple Vijaykumar, Luyu Wang, Piers Wingfield, Nat Wong. Keyang Xu, Christopher Yew, Nick Young, Vadim Zubov, Douglas Eck, Dumitru Erhan, Koray Kavukcuoglu, Demis Hassabis, Zoubin Gharamani, Raia Hadsell, Aäron van den Oord, Inbar Mosseri, Adrian Bolton, Satinder Singh, and Tim Rocktäschel. Genie 3: A new frontier for world models. ht /deppii and.. (teepmind..googeeee google/ blog/ 1elog/genie---88 new- Frontieresr.. for wor wwwlddmodels/. 2025.
Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. Multidiffusion: Fusing diffusion paths for controlled image generation. In International Conference on Machine Learning (ICML), 2023.
Shengqu Cai, Ceyuan Yang. Lvmin Zhang, Yuwei Guo, Junfei Xiao, Ziyan Yang, Yinghao Xu, Zhenheng Yang, Alan Yuille, Leonidas Guibas, et al. Mixture of contexts for long video generation. arXiv preprint arXiv:2508.21055 2025.
E.F. Camacho and C.B. Alba. Model Predictive Control. Springer London, 2013.
Hila Chefer, Uriel Singer, Amit Zohar, Yuval Kirstain, Adam Polyak, Yaniv Taigman, Lior Wolf, and Shelly Sheynin. VideoJAM: Joint appearance-motion representations for enhanced motion generation in vidco models. In International Conference on Machine Learning (ICML), 2025.
Boyuan Chen, Diego Martí Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitzmann. Diffusion forcing: Next-token prediction meets full-sequence diffusion. In Advances in Neural Information Processing Systems (NeurIPS), 2024.
Guibin Chen, Dixuan Lin, Jiangping Yang. Chunze Lin, Junchen Zhu, Mingyuan Fan, Hao Zhang, Sheng Chen, Zheng Chen, Chengcheng Ma. Weiming Xiong, Wei Wang, Nuo Pang, Kang Kang. Zhiheng Xu, Yuzhe Jin, Yupeng Liang. Yubing Song, Peng Zhao, Boyuan Xu, Di Qiu, Debang Li, Zhengcong Fei, Yang Li, and Yahui Zhou. Skyreels-v2: Infinite-length film generative model. arXiv preprint 2504. 30774, 2025a
Sili Chen, Hengkai Guo, Shengnan Zhu, Feihu Zhang, Zilong Huang, Jiashi Feng, and Bingyi Kang. Video depth tnything: Consistent depth estimation for super-long videos. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025b.
11

---

Published as a conference paper at ICLR 2026
Karan Dalal, Daniel Koceja, Gashon Hussein, Jiarui Xu, Yue Zhao, Youjin Song, Shihao Han, Ka Chun Cheung, Jan Kautz, Carlos Guestrin, Tatsunori Hashimoto, Sanmi Koyejo, Yejin Choi, Yu Sun, and Xiaolong Wang. One-minute video generation with test-time training. In IEEE Conference on Computer Vision and Puttern Recognition (CVPR), 2025.
Decart, Julian Quevedo, Quinn McIntyre, Spruce Campbell, Xinlei Chen, and Robert Wachen. Oasis: A universe in transformer. 2024. URL httpp:://asiiiiii moa.aaammollllll...
Ian Failes. The big effects moments in the one-shot '1917'. https:t/beeeereeeaaaaaaa com/ 0t1///22000111111
Salg-effects -ootstmoommtteeee in one-tnee--eeeeeee shot-hht771111
2020.
Hossein Goli, Michael Gimelfarb, Nathan Samuel de Lara, Haruki Nishimura, Masha Itkina, and Florian Shkurti. Stitch-ope: Trajectory stitching with guided diffusion for off-policy evaluation. In Advances in Neural Information Processing Systems (NeurIPS), 2025.
Google DeepMind. Veo-3-tech-report.pee Technical report, 2025.
Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang. Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. In International Conference on Learning Representations (ICLR), 2024.
Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Cameractrl: Enabling camera control for text-to-video generation. In International Conference Learning Representations (ICLR), 2025a.
Hao He, Ceyuan Yang. Shanchuan Lin, Yinghao Xu, Meng Wei, Liangke Gui, Qi Zhao, Gordon Wetzstein, Lu Jiang, and Hongsheng Li. Cameractrl i:: Dynamic scene exploration via camera- controlled video diffusion models. In IEEE International Conference on Computer Vision (ICCV), 2025b.
Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12500 2022.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems (NeurIPS), 2020.
Emiel Hoogeboom, Jonathan Heek, and Tim Salimans. Simple diffusion: End-to-end diffusion for high resolution images. In International Conference on Machine Learning (ICML), 2023.
Anthony Hu, Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, and Gianluca Corrado. Gaia-I: A generative world model for autonomous driving. arXiv preprins arXiv: 17080, 2023.
Xun Huang, Zhengqi Li, Guande He, Mingyuan Zhou, and Eli Shechtman. Self forcing: Bridging the train-test gap in autoregressive video diffusion. In Advances in Neural Information Processing Systems (NeurIPS), 2025.
Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang. Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, Yaohui Wang, Xinyuan Chen, Limin Wang, Dahua Lin, Yu Qiao, and Ziwei Liu. VBench: Comprehensive benchmark suite for video generative models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion- based generative models. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
Jaihoon Kim, Juii Koo, Kyeongmin Yeo, and Minhyuk Sung. Synctweedies: A general generative framework based on synchronized diffusions. Advances in Neural Information Processing Systems (NeurIPS), 2024.
Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03600 2024.
12

---

Published as a conference paper at ICLR 2026
Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao, Cheng Xin, Kun Wan, Lantao Yu, Qianyu Guo, Zixun Yu. Yawen Lu, et al. D13dv-0k:: A large-scale scene dataset for deep learning-based 3d vision. In IEEE Conference Computer Vision and Pattern Recognition (CVPR), 2024.
Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B Tenenbaum. Compositional visual generation with composable diffusion models. In European Conference on Computer Vision (ECCV), 2022.
Luma Al Team. Introducing dream machine: Luma's first generative video model, 2024.
Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard S. Zemel. Understanding the effective receptive field in deep convolutional neural networks, In Advances in Neural Information Processing Systems (NeurIPS), 2017.
Yunhao Luo, Utkarsh A Mishra, Yilun Du, and Danfei Xu. Generative trajectory stitching through diffusion composition. In Advances in Neural Information Processing Systems (NeurIPS), 2025.
Utkarsh Aashu Mishra, Shangjie Xue, Yongxin Chen, and Danfei Xu. Generative skill chaining: Long-horizon skill planning with diffusion models. In Conference on Robot Learning (CoRL), 2023.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In IEEE International Conference on Computer Vision (ICCV), 2023.
Lionel Sharples Penrose and Roger Penrose. Impossible objects: a special type of visual illusion. British journal of psychology, 49:31-3, 1958.
Runway. Introducing Runway Gen-4. 4 2025.
Lloyd Russell, Anthony Hu, Lorenzo Bertoni, George Fedoseev, Jamie Shotton, Elahe Arani, and Gianluca Corrado. Gaia-2: A controllable multi-view generative world model for autonomous driving. arXiv preprint arXiv:2503.20523, 2025.
Tim Salimans, Ian Goodfellow, Wojeiech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. In Advances in Neural Information Processing Systems (NeurIPS), 2016.
Sand.ai, Hansi Teng, Hongyu Jia, Lei Sun, Lingzhi Li, Maolin Li, Mingqiu Tang, Shuai Han, Tianning Zhang, W. Q. Zhang, Weifeng Luo, Xiaoyang Kang, Yuchen Sun, Yue Cao, Yunpeng Huang, Yutong Lin, Yuxin Fang, Zewei Tao, Zheng Zhang, Zhongshu Wang, Zixun Liu, Dai Shi, Guoli Su, Hanwen Sun, Hong Pan, Jie Wang. Jiexin Sheng. Min Cui, Min Hu, Ming Yan, Shucheng Yin, Siran Zhang, Tingting Liu, Xianping Yin, Xiaoyu Yang, Xin Song, Xuan Hu, Yankai Zhang, and Yuqiao Li. Magi-l: Autoregressive video generation at scale. arXiv preprint arXiv:2505.132111 2025.
Florian Schmidt. Generalization in generation: closer look at exposure bias. arXiv preprint 1rXiv:1910.00022,, 2019.
Manrel-Andreas Schneider, Lukas Höllein, and Matthias Nießner. Worldexplorer: Towards generating fully navigable 3d scenes. ACM SIGGRAPH Asia, 2025.
Jascha Sohl- -ohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning (ICML), 2015.
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Interna- tional Conference on Learning Representations (ICLR), 2021a.
Kiwhan Song, Boyuan Chen, Max Simchowitz, Yilun Du, Russ Tedrake, and Vincent Sitzmann. History-guided video diffusion. In International Conference Machine Learning (ICML), 2025.
Yang Song, Jascha
Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In International Conference Learning Representations (ICLR), 2021b.
13

---

Published as a conference paper at ICLR 2026
Dani Valevski, Yaniv Leviathan, Moab Arar, and Shlomi Fruchter. Diffusion models are real-time game engines. In International Conference on Learning Representations (ICLR), 2025.
Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng. Jiayu Wang, Jingfeng Zhang, Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li. Pingyu Wu, Ruihang Chu, Ruili Feng, Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang, Tianyi Gui, Tingyu Weng. Tong Shen. Wei Lin, Wei Wang, Wei Wang. Wenmeng Zhou, Wente Wang. Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang. Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong. Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu, and Ziyu Liu, Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv: 2503 2025.
Xiang Wang, Hangjie Yuan, Shiwei Zhang, Dayou Chen, Jiuniu Wang, Yingya Zhang, Yujun Shen, Deli Zhao, and Jingren Zhou. Videocomposer: Compositional video synthesis with motion controllability, In Advances in Neural Information Processing Systems (NeurIPS), 2023.
Zhouxia Wang, Ziyang Yuan, Xintao Wang. Yaowei Li, Tianshui Chen, Menghan Xia, Ping Luo, and Ying Shan. Motionctrl: A unified and flexible motion controller for video generation. In ACM SIGGRAPH, 2024.
Ethan Weber, Norman Müller, Yash Kant, Vasu Agrawal, Michael Zollhöfer, Angjoo Kanazawa, and Christian Richardt. Fillerbuster: Multi-view scene completion for casual captures. In International Conference 3D Vision (3DV), 2026.
Zeqi Xiao, Yushi Lan, Yifan Zhou, Wenqi Ouyang, Shuai Yang, Yanhong Zeng, and Xingang Pan. Worldmem: Long-term consistent world simulation with memory. In Advances in Neural Information Processing Systems (NeurIPS). 2025.
Virginia Yates. Rope
sets a precedent.
https://thiaacc.com/rtttie
teree-setsa--reeee
2023.
Kyeongmin Yeo, Jaihoon Kim, and Minhyuk Sung. Stochsyne: Stochastic diffusion synchronization for image generation in arbitrary spaces. In International Conference on Learning Representations (ICLR), 2025.
Chandan Yeshwanth, Yuch-Cheng Liu, Matthias Nießner, and Angela Dai. Scannet++: A high-fidelity dataset of 3d indoor scenes. In IEEE International Conference on Computer Vision (ICCV), 2023.
Tianwei Yin, Qiang Zhang, Richard Zhang, William T Freeman, Fredo Durand, Eli Shechtman, and Xun Huang. From slow bidirectional to fast autoregressive video diffusion models. In IEEE Conference Computer Vision and Pattern Recognition (CVPR), 2025.
Jiwen Yu, Jianhong Bai, Yiran Qin, Quande Liu, Xintao Wang, Pengfei Wan, Di Zhang, and Xihui Liu. Context as memory: Scene-consistent interactive long video generation with memory retrieval. In ACM SIGGRAPH Asia, 2025.
Tianyuan Zhang, Sai Bi, Yicong Hong, Kai Zhang, Fujun Luan, Songlin Yang, Kalyan Sunkavalli, William T. Freeman, and Hao Tan. Test-time training done right. arXiv preprint 280.22250,,,.884, 2025.
Jensen Zhou, Hang Gao, Vikram Voleti, Aaryaman Vasishta, Chun-Han Yao, Mark Boss, Philip Torr, Christian Rupprecht, and Varun Jampani. Stable virtual camera: Generative view synthesis with diffusion models. In IEEE International Conference on Computer Vision (ICCV), 2025.
Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. ACM SIGGRAPH, 37, 2018.
14

---

Published as a conference paper at ICLR 2026
A EXPERIMENTAL DETAILS
Algorithm 1: Camera-guided Video Generation with GVS
Inputs: cameras p: context windows { [wn)==0 1 each specified by Target chunk + 27 timesteps
Outputs: z: video sample of length T aligned with p
Function GVS (p, (wn)n=o))
2 ~ (0,1) W for 0, ..... do
Initialize video sample with pure-noise sequence
for n 0, ..... N do if w. is equal to tth target chunk then
Compile context windows containing ith target chunk
end
end
end
for
- 1..... ~ ((.1)
Sample pure-noise sequence for stochasticity term
for t 0,..., T do
w. + Willk mod [W[]]]
D. Cycle through context windows
x + fwp (z), pt + fwt (p), + (et
Project context window
Es - (1 + (xx - (eo(0. xt, 010.0.0)
b. Predict guided noise
olk xi ako
x,
Predict clcan sample
a² t- n ax-
Compute stochasticity
k-1 t a xx 1/ + / - a01--a1--000 Eo o
Run DDIM denoising step
end
z argmin, ΣT-1 [1 (z) 112
Update video sample with target chunks
end
A.1 IMPLEMENTATION DETAILS
Backbone Diffusion Models. In this paper, evaluate all sampling methods on three different backbone diffusion models: one Diffusion-Forcine (DF) backbone (see Table [ and two DF-free backbones (see Tables 7 and (). All three backbones support camera-conditioned video generation, are trained on the RealEstate 10K dataset (Zhou et al. 2018), and are opensourced by /Song et al. (2025). The DF-free backbones are each trained with frame-level binary-dropout (BD) diffusion, which is fully compatible with every component of GVS, and full-sequence (FS) diffusion (uniform noise levels across all frames), which compatible with GVS, except for Omni Guidance. The full implementation details of these backbones can be found in Song et al. (2025), but we repeat the most relevant points for completeness" sake:
All three backbones share the same U-ViT architecture (Hoogeboom et al. 2023) (with 459M parameters) that accepts 256 x 256 video inputs. All conditioning signals i.e., per-frame noise levels and camera poses, are injected into the model via Adaptive LayerNorm. Importantly, camera conditioning is injected by first computing relative poses with respect to the first frame and then transforming them into high-dimensional ray encodings.
Sampling. For history-guided autoregressive sampling (Song et al. 2025), we use the default hyperparameters from its open-sourced implementation: a deterministic DDIM sampler, which corresponds to setting o² = 0 in Eq. [ with a linear denoising step schedule over 50 sampling steps (the number of training denoising steps is 1000), history guidance scale of 4, which controls the joint guidance on history and camera conditioning, 4 history frames per context window, and a stabilization level of 0.02.
We repurpose the StochSync baseline YYoo et al. 2025). originally designed for arbitrary images such as 3e0-degree panoramas and 3D mesh textures, for camera-guided video generation; we treat the
15

---

Published as a conference paper at ICLR 2026
Panorama 1-loop
Circle 1-loop
Staircase Circuit
model
F
AAAA
4000
23
T-
23
Spatial Window
Spatial Window
Spatial Window
Spatial Window
Spatial Window/
Spatial Window
Panorama 2-loop
AAVA
47
T= 17
T= 47
Spatial Window
Spatial Window
Spatial Window
Spatial Window
Spatial Window 11
Spatial Window 12
Circle 2-loop
47
T=
47
Spatial Window Spatial Window
Spatial Window
Spatial Window
Spatial 11 Spatial Window
Figure 8: Spatial Context Windows that Define Our Method's Cyelic Conditioning Strategy. Note that for Panorama 1-loop and Panorama 2-1oop, we intentionally offset the camera centers from the panorama's rotation center better distinguish between different parts of conditioning trajectory. We visualize Circle 1 1-1cop and Circle 2-1oop as spirals also for better clarity.
video output from our backbone model as a wide image generated by an image diffusion backbone. We use the default hyperparameters from StochSync's 's implementation for 360-degree panorama generation: a maximum stochasticity DDIM sampler (a = v1 - a linear denoising step schedule over 25 sampling steps from K 900 to Ksopp 270, multi-step computation of clean samples, which is initially run for 50 steps and linearly decreased as the outer-loop denoising progresses, and two alternating sets of non-overlapping context windows, which are offset from each other by 4 frames. While the default guidance scale is 7.5, we find that this leads to unrealistic generations for the given backbone and task and therefore we lower the guidance scale to Note that this guidance scale only controls camera conditioning as StochSync does not guide with history.
Our method (GVS) is outlined Algorithm 1 where E {0, 1,......, T 1} indexes the /th target chunk, which is comprised of timesteps { (Tunttschh))))) +1)-1 and Tarrget chunk Trovelap, T denote TiTiiggtauns
the size of each target chunk, overlap length between context windows, and target video length respectively; w denotes the target chunk of context window w, and (w(2) denotes the selection of frames from video estimate that lic within
Our method uses a partial stochasticity DDIM sampler (a = = with stochasticity level 0.9 and a guidance scale of 7 1. Note that our guidance scale convention represents no guidance with y = O, whereas the baseline methods represent no guidance with 7 1. Our method employs overlapping context windows with Troeriap = 2 and tagget chunk 4. In other words, each 8-frame-long context window is comprised of frames from the past chunk, the target chunk, and frames from the future chunk. We visualize this in Fig. 2 but with half the context window size.
16

---

Published as a conference paper at ICLR 2026
Indefinite Staircase
0
1079
474
954
==0
Figure GVS Stably Scales Longer Videos Given More Test-Time Compute.
Loop-Closing Mechanism. For conditioning camera trajectories that require loop-closing ie., Panorama 1 1-loop, Panorama 2 2-1oop, Circle 1-loop, Circle 2-1oop, and Staircase circuit, we equip all sampling methods with a loop-closing mechanism.
Inspired by Xiao et al. (2025): Zhou et al. 22055), we augment history-guided autoregressive sampling with memory mechanism that retrieves previously generated frames whose field-of-view overlap with the current generation exceeds a threshold. At every autoregressive step, we generate I frame conditioned on 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 StochSyne with our cyclic conditioning mechanism, which we tailor to its non- overlapping window sampling strategy. For trajectories that form single loop ie., Panorama 1-1oop, Circle 1-1cop, and Staircase circuit, use StochSync's original strategy of alternating between two sets of context windows, which a form cyclic conditioning: the second set includes context window that "wraps around" the loop and thus enforees consistency between the start and end of the camera trajectory. For Panorama 2-1oop and Circle 2-1oop, we add third set of non-overlapping context windows that enforces consistency between corresponding frames in the first and second loops, similar to "Spatial windows 1 ~ 12" in Fig. g
Our method loop closes via cyclic conditioning, whereby target chunk f. is denoised by two alternating sets of context windows: 1) temporal context windows, which contain timesteps
(T) ((7 tonut +1)+7mate=1 and 2) spatial context windows, which we summarize in Fig.
A.2 DETAILS ON THE IMPOSSIBLE STAIRCASE (SEC. 4.3
To loop close the two end points the Impossible Staircase trajectory, we use a cyclic conditioning technique similar to that of Staircase Circuit, which is shown in Fig g The key difference is that we modify the conditioning camera segments for the two spatial windows, which condition the first frame the last frame and vice versa; replace the original camera segments, which are disconnected due to the height difference between the end points of the trajectory, with a continuous straight line to encourage visual continuity.
17

---

Published as a conference paper at ICLR 2026
300
AR
AR
Arenue Wilkhhe Time pee (EL)
SunchSync CVVS (ours)
StxchSyne (ouns)
GPF1 Momory Allscutd
100
200
80-
200
40
108
Number Frames
Number of Frames
Figure 10: Inference Costs Function of Target Sequence Length.
Time
Space
24
T
120
Complexity Complexity
wall
mallec
wall
malloc
wall
malloc
wall
malloc
wall
malloe
AR
O(BTK.)
O(BT)
36.19
12.37
86.51 14.63
134.8 16.62 181.88 19.87
212.52
22.46
AR
22.60
32.74
45.94
53,40
72.15
76.50
96.36
100.22
119.66
124.05
StochSync O(BTK))
O(BT)
116 29
27
29
212.60
44.54
311
68
58.58
404.29
74.78
504.80
90.98
GVS (Ours) O(BTK,)
O(BT)
24.20
32.62
47.69
$2.66
71 46
74.87 95.33
97.07
118.10
119.27
Table 4: Theoretical Complexity and Empirical Performance. denotes batch size, Ke denotes the number of sampling timesteps, and 7 denotes the number of diffusion windows to be processed, which is directly proportional to the target sequence length T for a fixed context window. All methods batch size B 1, except for AR which has batch size B = T to match the memory costs of GVS. "wall time" denotes the average wallclock time taken generate a single video (units: seconds / video) and "malloc" denotes the GPU memory allocated (units: Gb), respectively.
A.3 COMPUTE RESOURCES
We run every experiment on single NVIDIA H200 GPU and report the resulting metrics. We also provide a scalable implementation of GVS that be run on lower-VRAM GPU, such as the NVIDIA RTX A6000. At every denoising step, the scalable implementation denoises every context window one-by-one, whereas the default implementation denoises all context windows in parallel.
B ADDITIONAL EXPERIMENTAL RESULTS
B. APPLICATION: THE INDEFINITE STAIRCASE
In Fig. 9 further highlight the long-horizon stability of GVS by generating 1080-frame video that climbs an 18-story staircase (here there is no loop closure between the end points of the conditioning trajectory). The entire video collision-free free and stable, demonstrating GVS' scaling properties and its potential as an alternative to autoregressive extension for long video generation.
B.2 INFERENCE COST
In Fig. and and Table 4 we compare the theoretical complexity and empirical inference costs of GVS and the baselines. To measure the empirical performance, we consider a video generation task on the Straight Line trajectory of varying lengths T [24, 48, 72, 96, 120) with batchsize B 1 and measure 1) "wall time". the wallclock time to generate a single video averaged over 40 samples and 2) "malloc", the GPU memory allocated.
Autoregressive (AR) sampling incurs linear time complexity in T, the number diffusion windows be processed, as it processes diffusion windows sequentially, one-by-one. This implies linear time
18

---

Published as a conference paper at ICLR 2026
Panorama 2-loop
Sample
Sample 2
0..
Figure 11:
Lowering Stochasticity Levels Reduces Oversmoothing. Note that intentionally offset the camera centers from the panorama's 's rotation center to better distinguish between different parts of the conditioning trajectory.
Omni Guide
Panorama
2-1oop
F3PC(L)
(.O())
1Q(t)
AQ(T)
18(1)
0.9 0.166
0.133
0.402
0.355
1.59
X
0.8
0.175
0.147
1.56
0.9
0.153
0.116
0.483
0.376
2.03
0.8
0.161
0.113
0.488
0.389
2.41
Table 5: Omni Guidance Provides Additional Affordance to Reduce Oversmoothing.
complexity in the target sequence length T, which is directly proportional to the number of diffusion windows to be processed T for fixed context window. AR sampling incurs linear space complexity in T and in T, it has store the outputs of each diffusion window. GVS, which also has linear time and space complexity T. enables parallelization along the temporal dimension: unlike AR sampling. GVS can stack all 7 diffusion windows along the batch dimension and simultaneously process them in a single forward pass through the backbone diffusion model. In other words, for the same batch size B, GVS enables shorter runtimes than AR sampling the cost of increased memory usage. This is useful in minibatch or single-sequence settings that prioritize low batch-level latency, such interactive one-shot cinematography where users iteratively refine a single target video.
Note that AR sampling can also be parallelized for better GPU utilization by generating multiple videos in batches, which reduces the average runtime per video the expense of increased memory usage. In fact, when we set batchsize of AR sampling as B T (we denote this method AR+), the average runtime and memory usage is virtually the same as that of GVS. However, AR+ achieves worse batch-level latency than GVS, especially for longer target sequences, not to mention that for our problem setting AR* also results in collisions with the generated scene.
StochSyne is also parallelizable across the temporal dimension, but it is even slower than AR sampling due to its quadratic complexity in Ke, the number of sampling timesteps.
B.3 ABLATION ON OMNI GUIDANCE AND STOCHASTICITY
In Fig. [ and Table 5 further demonstrate how Omni Guidance provides additional affordance to reduce oversmoothin without sacrificing consistency. Fig. [ demonstrates that for P Panorant 2- 1 2-1oop, the default stochasticity level y 0.9 results oversmoothed generations, which can be
19

---

Published as a conference paper at ICLR 2026
Full Sequence Diffusion
Binary Dropout Diffusion
Diffusion Forcing
Trajectory
FIPOL) LRC()) TQ(1) AQX)) CACL) P(RC()) (RC() 1Q(1) AQ(1) CAC P2PC()) LRC()) TQ(1) AQ(T) CAC))
Panoreme
1oloop
0.138
0.228
0.391
0.429
N/A
0.130 0.136 0.557 0.486
NA
0.188 0.341
0.537 0.461 N/A
Panozama 2-1oop
0.166
0.139
0.332
0.384
N/A O. 137 0.100 0.443
0.391
N/A 0.155
0.116
0.483 0.376 NIA
Circle Toop
0.169
0.349
0.460
0.409
0.165 0.283 0.538 0.465
0.025
0.160
0:244
0.546
0.432
Circle 2-1oop
0.201
0.245
0.386
0.379
0.025
0.170 0.187 0.470
0.378
0.025
0.182
0.206
0.465 0.358
Straight
0.080
Staics
0.587
0.132 NIA 0.604 0.420
0.137
0.621
Stairease circuit
0.143
0.354 0.520 0.411
0.133 0.171 0.603 0.448
0.129
0.178
0.607 0.419
Table 6: Ablation on Backbone Models.
Autoregressive
StochSync
Ours (GVS)
Trajectory
FIFC)) LRC() 1007) AQCO CAJ) FZFC(J) LRC(L) 10(t) AQC CA(D) FIF(U) LRCY)) 10(T) AQX?) CMQ))
Panozama 2- 1-1oop
0.157
0.338
0.524
0.44G
NA
0.199 0.222
0.500
0.496
NA 0.139 0.136 0.657 0.466 N/A
Panotama 2-1
0.167
0.224
0.662 0.442 N/A 0.280 0.449 0.456 0.464
NIA 0.137 1.100 0.443 0.391
N/A
Circle 1 1-lopp
0.180
0.489
0.517
0.414 0,350 0.206 0.258
0.511
0.464
0.0 0.166 0.283
0.688
0.455 D.026
Circle 2-1oop
0.300
0.547
0.432
0.475
0.294
0.494
0.478
0.488
0.170
0.187
0.470
0.378 0.025
Straight 1ine
0.123
N/
0.469
0.372
0.650
0.138
0.636 0.403
0.076
0.482
1884 N/A
0.366
0.400
0.214
0.563
0.132
0.604 0.420
Staircase circust
0.392
0.429 0.456 0.317 0.900 0.207
0.248
0.587
0.444 0.025
0.133
0.171 0.603 0.448 0.0
Table 7:
Comparison with Baselines on Backbone Trained with Binary-Dropout (BD) Diffusion.
alleviated by reducing the stochasticity level yj 0.8. Table shows that stochasticity can be reduced without sacrificing consistency due to the graceful tradeoff properties afforded by Omni Guidance (compare rows 3 and 4); without Omni Guidance, reducing stochasticity hampers both temporal and long-range consistency (F2FC, LRC).
B.4 ABLATION ON BACKBONE DIFFUSION MODELS
To investigate the generalizability of GVS other backbone diffusion models, we perform additional experiments on two Diffusion- Diffusion-Forcing-ffe Forcing-free backbones that share the same architecture and training data as the DF backbone used the main paper. The details of these DF-free backbones (BD backbone and FS backbone) can be found in section A.I
Table Fig. 15 demonstrates that GVS can also be applied DF-free backbones; GVS generates long-horizon rollouts that are stable, collision-free and reasonably consistent. It should be noted that since the FS backbone does not support Omni Guidance, GVS displays slightly worse consistency on the FS backbone than the DF backbone BD backbone, which do support Omni Guidance.
Furthermore, the experimental takeaways for the DF backbone from sections 4.] and 4.2 also hold for its DF-free counterparts: Tables 7 and g and Figures 16 and 17 show that GVS outperforms AR sampling in terms collision avoidance and consistency and StochSync in terms temporal consistency. In particular, for the FS backbone, AR sampling collapses beyond the first context window since does not support conditioning on context frames (as also demonstrated by SSng al. 2025)), whereas GVS generates reasonably consistent videos that stable and collision-free..
20

---

Published as a conference paper at ICLR 2026
Autoregressive
StochSyne
Ours (GVS)
Trajectory
FIPOL) LRC()) (Q(1) AQC)) CAC)) FIFOL)) LRC()) fQrt)
AQCT) CA(C) FIFCL)) (RC()) rQ(t) AQ(1) CA(L)
Panorane 1. Loop
0.528
0.846 0.370 0.334 N/A 0.196 0.191 0.484
0.485
N/A
0.138 0.228 0.391 0.429 N/A
Panoroms
2-2cop
0.515
0.720
0.335
0.289
0.800 0,288 0.449
0.474 0.444
N/A
0. 066
0.139 0.332 0.384 N/A
Girole
loop
0.523
0.890
0.372
0.320
0.800 0.199
0.271
0.581
0.449
0.0 0.169 0.349
0.460
0.409 0.0
Circle 2- loco
0.494
0.716
0.328
0.960
0.275
0.441
0.502
0.427
0.0 0.201 0.245
0.386 0.379 0.025
Straight line
0.526
0.198
0.626 0.415
0.587 0.360
Staircace circuit
0.644
0,369 0.331 0.273 0.950 0.197 0.222 0.583 0.442 0.025 0.143 0.354 0.520 0.411 0.0
Table 8: Comparison with Baselines on Backbone Trained with Full-Sequence (FS) Diffusion.
Straight
line
Stairs
Strriight
1ine
Stairs
1
?7
100t)
AUrt)
(S(t)
AUrt)
0.150
0.494
0.458
1.79
0.204
0.512
0.422
2.09
0.186
0.499
0.187
0.645
0.450
0.5
0.116
0.446
0.416
1.68
0.178
0.499
0.411
1.78
0.5
0.114
0.552
0.401
2.67
0.170
0.551
0.458
2.94
0.9
0.080
0.442
0.413
1,58
0.137
0.502
0.401
1.56
0.9
0,076
0.625
0. 4.43
1.65
0.132
0.804
0.420
2.16
0.615
0.494
0.121
without Omni Guidance
(b) with Omni Guidance
Table 9: Ablation on Omni Guidance and Stochasticity for the BD backhone.
Not only that, Table 9 demonstrates that for the BD backbone, which is fully compatible with every component of GVS, Omni Guidance enhances temporal consistency across wide range of stochasticity levels, providing additional flexibility to reduce oversmoothing.
These results suggest that GVS can potentially be applied wide range of off-the-shelf video diffusion models, regardless of their training framework.
B.5 ABLATION ON SAMPLING TIMESTEPS
In Tables 10 and II we examine the effect of varying the number of sampling timesteps K, [25, 50, 100, 200]. Since the theoretical receptive field of each target chunk grows with every denoising step. one could reasonably expect consistency to constantly improve as K₂ increases.
Surprisingly, while increasing K₂ low values generally improves frame-to-frama (F2FC) and long-range consistency (LRC), at higher values consistency deteriorates. We attribute this trend to the observation that increasing K2 also generally increases scene complexity (as reflected in the general improvement of video quality metrics IQ, AQ, and IS), making the task of consistency a harder one. Furthermore, we also find that increasing the number of sampling timesteps does not obviate the need for our explicit loop closing mechanism (see Table TO). We therefore use intermediate value K2 50 as the default, which balances consistency, scene complexity, and inference costs (for GVS, time and space complexity is linear in K,, as stated in Table 4).
B.6 ABLATION ON CHUNKING PROFILE
In Table 12 we investigate GVS' sensitivity to the chunking profile by varying the target chunk size turget chink and the overlap length overrlp between neighboring windows. Each context window is
comprised of Taagee chunk frames in the target chunk, flanked by T frames on both sides. It can be seen that increasing Trwwellp, or equivalently reducing Target chunk generally enhances temporal consistency but also increases scene complexity demonstrated by
2 past and future conditioning
21

---

Published as a conference paper at ICLR 2026
Panorama 1 1-1oop
Panorama 2 2-1oop
Panorama 2-1oop
Panorama 2-loop
K,
K
LRCY)
1X1:
NO01
(2FC:))
(ROU)
1001)
NO01)
(2PC4))
LRCU)
F2F0())
(RCI)) 1001) AQrh
25
0.140
0.917
0.518
0.451
0.341
0.918
0.518
0.455
0.341
0.191
0.532
0.449
0.363
0.163
0.418
0.366
50
0.141
0.962
0.554
0.463
0.140
0.917
0.546
0.461
50
0.188
0.141
0.537
0.461
0.155
0.116
0.463
0.376
100
0.948
0.560
0.464
0.148
0.567
0.147
0.558
0.466
0.109
0.514
0.373
200
0.156
0.988
0.689
0.472
0.158
0.916
0.578
0.480
200
0.154
0.143
0.671
0.476
0.148 0.110
0.650
0.368
(a) without Loop Closing
(b) with Loop Closing
Table 10: Ablation on Sampling Timesteps.
Straight line
Stairs
Kg
P2FOL) 10(1) AQX1)
18(1) (2FO())
10(1) AQ(1)
1S(1)
25
0.089
0.566
0.423
1.53
0.140
0.575
0.394
1.70
50
0.080
0.615
0.423
1.65
0.137
0.621
0.401
1.66
100
0.078
0.640
0.422
1,60
0.402
1.73
200
0,080
0.813
0.439
1.58
0.149
0.609
0.413
2.18
Table 11: [Continued] Ablation on Sampling Timesteps.
T largel chunk
Toeerapp
Straight Line
Stairs
(2FC(()
10rt)
AQT)
18(1)
FIFOL)
KXT)
AQ(T)
1S(1)
2
0.087
0.510
0.396
1.65
0.147
0.583
0.385
1.50
0.079
0.616
0.421
1.52
0.188
0.618
0.404
1.67
2
0.089
0.624
0.429
1.91
0.136
0.620
0.449
2.41
Table 12: Ablation on Chunking Profile.
the improvement in video quality metrics (IQ, AQ, and IS). For Stairs this does not affect the trend of enhanced temporal consistency, but for Straight Line, increasing Troverlap eventually worsens consistency as increased scene complexity makes the task of consistent generation a harder one. For all of our main experiments, we therefore choose intermediate values laaget chunk = 4 and Trverrap = 4, which balances consistency, scene complexity, and inference costs (for given target sequence length 'T, reducing target chunk increases the number of context windows to be processed T, resulting in higher inference costs, as shown Table 4 and Fig. 10
B.7 ADDITIONAL QUALITATIVE COMPARISONS WITH BASELINES
In Fig. 18 T9 and 20
visualize additional qualitative comparisons with baselines, which corrobo- AR sampling collides with the generated scene, after which generation collapses. and often fails close loops. StochSync avoids collisions, but achieves this by shape- shifting the scene. Our method, GVS, generates stable and temporally consistent samples that are collision-fie free and closes loops.
rate our findings in Sec. I
C LIMITATIONS, CHALLENGES, AND FUTURE WORK
C.1 APPLICATION TO ONLINE SETTINGS
GVS achives collision-free camera-guie -uided video generation by breaking causality, which precludes GVS from being applied online problem settings where future camera poses not available, such as interactive video generation and streaming.
One interesting direction for future research is to extend GVS' stitching approach to the online problem setting and enable collision avoidance during interactive generation and streaming. Such method would be the generative analog of model predictive control (Camacho & Albal 2013)
22

---

Published as a conference paper at ICLR 2026
RE10K- Mini Sample (24 frames)
Denoising
000
730
k
270
A
Figure 12: GVS Struggles to Propagate External Context Frames Throughout the Entire Video.
ie., model predictive generation: at each autoregressive step, diffusion stitching would be used to generate sequence longer than the context window conditioned on history and "future" camera poses, after which the future portion of the generated frames would be discarded. These "future" camera poses could be defined, for example, with motion extrapolation methods that favor open space and smoothness or a learned model that predicts user camera controls as a function of history.
C.2 EXTERNAL. IMAGE CONDITIONING
We show that GVS does not effectively propagate externaly provided context frames to the rest of the target video. In Fig. 12 we apply GVS to single-image-tur to-gge-to-video task defined by a context frame and corresponding camera trajectory sampled from the RE10K-Min dataset (Song al. 2025
and visualize the clean- sample predictions at select denoising steps. After noise level 1: 999. frame commits the scene depicted in the context frame 0, while the remaining frames are ambivalent. However, by noise level k 759, future frames r = 12 and beyond commit to a different scene, as there is direct information propagation from the context frame. By the end of the stitching process, the context frame has only propagated to frame r = 4, resulting in the awkward transition between these two diverged scenes as shown in frames
and
Note that GVS without external context frames avoids divergent scenes neighboring frames can affect each other throughout the stitching process; on the other hand, external context frames are unaffected by the rest of the target video. Autoregressive sampling also precludes divergent scenes as new generations are conditioned on fully denoised history.
Controlling information propagation in stitching by modulating the per-frame noise levels of the Ceffusion-Forcing backbone could potentially enable external image conditioning, which would not only improve video quality and user controllability, but also enable extending GVS to online problem settings via model predictive generation (see section C.I.. We leave this investigation for future work.
C.3 LOOP-CLOSING WIDE-BASELING VIEWPOINTS
We find that GVS fails loop-close wide-baseling viewpoints. To probe the root cause, we benchmark GVS on the Bawwardorbii-Bea -Backwardd trajectory, which is comprised of collinear forward and backward camera segments that connected by a 180-degree orbit camera segment, shown
23

---

Published as a conference paper at ICLR 2026
Forward- Barbk-dd (24 frames)
mndel Condianinn
w/o Loop Closing
w/ Loop Closing
Figure 13: GVS Fails to Loop-Close Wide-Baseline Viewpoints. Note that the camera centers of the forward and backward segments, which are collinear in implementation, visually offset for better clarity.
Forward-Orbit-Bel Orbit-Backward. Subtrajectory (8 frames)
Single-Window Diffusion
Time
Figure 14: Catusion-FForingg Backbone Fails on Wide-Baseline Camera Trajectories. Note that the camera centers, which collinear in implementation, visually offset for better clarity.
Fig. 13 This experiment suggests that this failure mode stems from the fact that our method's backbone trained on REIOK, which is comprised of camera trajectories with small viewpoint shifts.
To elaborate, without our loop closing mechanism, GVS generates a video that correctly tracks the camera motion i.e. depicts the generated scene from the correct viewpoint, but whose forward and backward segments do not view the same scene; the brown countertop present the right side of 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, but incorrectly do so from the same viewpoint, as opposed to opposite viewpoints. This is because the spatial context window
design for cyclic conditioning, shown in the top-right of Fig. [3 contains wide-baseline cameras in the conditioning trajectory. which is out-of-distribution the backbone model. Fig. 14 shows that applying single-window diffusion on this conditioning trajectory results in the same failure mode, where the generated video fails to track the camera motion. This failure mode may be addressed by training a Diffusion MorriinnnFooiingggg backbone on multi-view datasets with wider baselines, such as DL3DV (Ling et al. (2024) and ScanNet++ (Yeshwanth et al. 2023). which is another promising direction for future work.
C.4 SCALABLE CYCLIC CONDITIONING
The spatial neighbors in our proposed cyclic conditioning mechanism are manually defined per trajectory (see Fig. 8). To scale cyclic conditioning to arbitrary trajectories, one could potentially learn separate model that selects spatial neighbors based their noisy frames and/or the field- of-view (FOV) overlap (Xiao et al. 2025) of their conditioning cameras. We leave this line of investigation for future work.
24

---

Published as a conference paper at ICLR 2026
C.5 STRUCTURALLY SIMILAR CAMERA TRAJECTORY SEGMENTS
In some corner cases, GVS struggles to distinguish camera trajectory segments with similar structure. This is due the limited context of the Diffusion-Forcing backbone and its use of relative poses for camera conditioning. Fig. presents a notable example, where the start of the upward staircase is identical to the end of the downward staircase up to rigid transformation. Due to this ambiguity, GVS often generates small set of ascending steps at the bottom of the downward staircase (note that, otherwise, the generated video faithfully tracks the descending camera motion). Extending GVS to accept additional forms of conditioning, such as context images and text, could help resolve this ambiguity.
25

---

Published as a conference paper at ICLR 2026
Straight Line
Stairs
Panorama
2-loop
Time
Time
Time
FS
BD
Figure 15: Ablation Backhone Models. DF denotes the Eiffusion-Foreicnn backbone, BD denotes the Diffusion backbone, and FS denotes the Www---eeueee-Dffussee backbone.
Straight Line
Stairs
dessed pocer 0
Panorama
2-loop
+
Time
Time
Time
StochSym Sync Autoreg.
GVS
Figure 16: Qualitative Comparison with Baselines on Backbone Trained with Binary-Dropout
(BD) Diffusion.
Straight Line
Stairs
Panorama
2-loop
Time
Time
Time
StochSync Autorag.
GVS
Figure 17: Qualitative Comparison with Baselines on Backbone Trained with Full-Sequence (FS) Diffusion.
26

---

Published as a conference paper at ICLR 2026
Straight Line (48 frames)
Sample
Sample 2
Time
Time
StochSync Autorgg.
Stairs (24 frames)
a c
ee c
+
Sample 1
Sample 2
Time
Time
StochSync Autong
GVS
Panorama Panorama-lloop (24 frames)
Sample
Sample
Time
StockSync Autoreg.
GVS
Figure 18: Additional Qualitative Comparisons with Baselines. Note that intentionally offset the camera centers from the panorama's 's rotation center to better distinguish between different parts of the conditioning trajectory.
27

---

Published as a conference paper at ICLR 2026
Circle-1loop (24 frames)
Sample 1
Sample 2
Time
Time
StockSync Autoreg.
GVS
Panorama 2-loop (48 frames)
Sample
Sample
Time
Time
StochSync Autorng
GVS
Circle 2-loop (48 frames)
Sample
Sample 2
Time
Time
StochSync Autorig
GVS
Figure 19: [Continued] Additional Qualitative Comparisons with Baselines. Note that 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 2-1oop as spirals also for better clarity.
28

---

Published as a conference paper at ICLR 2026
Staircase Circuit (120 frames)
ge
Goe
E6
Sample
Sample
Time
Time
Autoreg
StockSync
Figure 20: [Continued] Additional Qualitative Comparisons with Baselines.
29


Reviews:
[{"review_id": "tJ6sojxFHe", "reviewer": "Reviewer_dQKs", "summary": "The paper introduces a camera conditioned video generation method via a parallel sampling strategy that circumvents shortcomings in autoregressive sampling, and proposes strategies for improving temporal consistency.", "questions": "* Does the framework support rolling out beyond 120 frames? \n* What's the size of the dataset? Is it 40 generations per trajectory or in total (line 394)?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* Presentation is very clear. \n* Several key challenges are identified, including collision, temporal consistency, etc., and are addressed by corresponding techniques proposed in the paper. \n* The framework is lightweight.", "weaknesses": "* Inference costs and their comparisons with baselines on computation complexity are not reported. \n* Metrics that specifically measure temporal consistency could be reported to strenghthen the claim on improvements along this aspect. \n* Ablations are conducted on one hyperparameter, stochasticity $\\eta$. Is the method robust to other hyperparameter choices?\n* The paper is restricted to pre-defined camera trajectories. Other limitations are also not discussed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8KwgfRlXEu", "reviewer": "Reviewer_Hd12", "summary": "This paper tackles a critical failure mode in camera-guided video generation: when using a predefined camera trajectory, autoregressive (AR) models often generate scene content (e.g., a wall) that the camera is later forced to \"collide\" with, causing the generation to collapse. AR methods are blind to future conditioning. The authors propose Generative View Stitching (GVS), a non-autoregressive sampling algorithm that generates all parts of the video sequence in parallel, making the entire generation faithful to the full camera trajectory. The authors note that video models trained with Diffusion Forcing already possess the necessary affordances for a training-free stitching algorithm. This is achieved by jointly denoising overlapping chunks, where a target chunk is conditioned on its co-evolving, noisy neighbors. The authors propose a novel guidance technique, Omni Guidance, which modifies the sampling step to significantly enhance temporal consistency between stitched chunks. This guidance is crucial for making the stitching process coherent. A Cyclic Conditioning mechanism is used to enable long-range consistency and loop closure. This is achieved by alternating the denoising process between temporal windows (conditioning on immediate neighbors) and spatial windows (conditioning on temporally distant but spatially close frames, like the start and end of a loop).", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper identifies a significant and practical limitation of current video generation models. The \"collision\" problem in predefined camera-guided generation is a major barrier to using these models for cinematography, simulation, or any form of high-level planning.\n- The central idea that the Diffusion Forcing training paradigm inherently enables a powerful, training-free inference-time stitching algorithm is reasonable. It repurposes a training-time feature (conditioning on variably-noised context) to solve a new inference-time problem.\n- Decent improvements upon baselines can be observed from experiments.", "weaknesses": "I do not have many problems with the technical part of the paper, but I do have concerns about the motivation and selling point of the paper. The paper puts itself as an improvement upon AR video generation models, so that the collapsing issue can be resolved.\n\nHowever, the approach necessarily breaks causality. In other words, we won't be having the core motivations of benefits of AR video generation anymore:\n- Efficiency because of KV cache/smart use of history.\n- \"Streaming\" ability, where we do not have access to future contents.\nBy breaking causality, of course we won't be running into the collapsing issue when cameras are given, as in other camera-controlled work --- because we have knowledge to the future already. This is a point that seems trivial to me.\n\nOverall, while the method is sound to me, the motivation/story part of the paper seems unconvincing to me. Would be good if the authors can make it clear.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xU97mYlnLr", "reviewer": "Reviewer_rey1", "summary": "The paper proposes Generative View Stitching (GVS), a training-free sampling method for camera-guided long-video generation that synthesizes all frames in parallel rather than autoregressively. GVS targets a key failure mode of autoregressive (AR) video diffusion—collisions and collapse when the predefined camera path would “walk through” previously generated content because the model cannot see the future. GVS divides the sequence into overlapping diffusion windows, jointly denoises target chunks with their past and future neighbors using any Diffusion Forcing (DF) video model. Two additions make this work for video: Omni Guidance (a guidance rule that strengthens conditioning on past & future within DF) and a cyclic conditioning mechanism for loop closure (long-range consistency).", "questions": "1. How does GVS conceptually differ from prior camera-controllable or pose-guided video diffusion methods?\n\n2. Can you provide more details about the DF backbone (architecture, conditioning format, training data)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Address the future-conditioning problem in camera-guided video and adapts diffusion stitching to video without retraining, leveraging DF’s token-wise noise masking. \n\n2. The Omni Guidance formulation modifies the joint score toward a conditional score using DF’s null-conditioning, and cyclic conditioning explicitly addresses loop closure, both are new in this context.\n\n3. Enables stable, collision-free, long camera-guided rollouts, including looped or topologically tricky paths (e.g., “Impossible Staircase”), using off-the-shelf DF video models, which lowers the barrier to long-video generation without retraining.", "weaknesses": "1. Limited discussion of related work in camera control.\nWhile the paper introduces a strong motivation around camera-guided video generation, it provides little discussion of prior camera control or camera trajectory-conditioned video synthesis methods. Recent works such as VideoComposer [1], CameraCtrl [2] have explored related spatial or pose-guided control mechanisms.\n\n2. Lack of comparison with camera-control-based baselines. No quantitative or qualitative comparisons are made with existing camera-controllable video diffusion methods, making it hard to judge relative controllability and spatial consistency.\n\n3. Missing ablation study on Diffusion Forcing (DF) effectiveness.  An ablation contrasting DF-free and DF-enabled variants would clarify how much DF drives the gains.\n\n4. Insufficient model description and unclear generality across base models.\nThe paper under-specifies the DF backbone, leaving ambiguity about architecture and conditioning. It is also unclear whether results hold on stronger base models like Wan 2.1 [3].\n \n5. Restricted applicability to Diffusion Forcing models.\nThe proposed method is tightly coupled to the Diffusion Forcing architecture, and cannot be directly applied to other video diffusion architectures.\n\n[1] \"VideoComposer: Compositional Video Synthesis with Motion Controllability\". \n[2] \"CameraCtrl: Enabling Camera Control for Text-to-Video Generation\". \n[3] \"Wan: Open and Advanced Large-Scale Video Generative Models\".", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rnJ3qmYcAY", "reviewer": "Reviewer_xADU", "summary": "This paper presents a method for video stitching with diffusion models, addressing issues of temporal consistency and scene realism. The proposed approach uses the existing Diffusion Forcing technique as a basis for conditioning of generated chunks of the video on neighboring chunks, both in the past and in the future. The proposed Omni Guidance technique also allows explicit loop closing by conditioning video chunks on both temporally and spatially related neighbors. \n\nThe benefits of the method proposed in the paper are demonstrated on a camera-path guided diffusion task, producing videos which are able to realistically follow a pre-defined path, generating in a way that allows future steps to be consistent with the path as well.", "questions": "1. For cyclic conditioning (section 3.5) - does conditioning the target chunk on spatially related chunks require users to manually find *all* spatially related chunks in the entire trajectory in advance? This seems doable with cameras looking forward in movement trajectories, or outwards in panoramas, but wouldn’t this be infeasible with non-trivial camera angles?\n2. Have the authors verified that the provided camera trajectories are compatible with the type of data present in the model training corpus (in particular, the stair trajectories)? If these were designed to match the data, what do results look like for trajectories not directly represented in the dataset?\n3. In what format are camera poses represented for conditioning? \n4. Line 460: “… suggests that the effective receptive field of GVS is local, not global.” This seems to be an effect of the “consistency propagation” as described in section 3.5. How many denoising steps are performed to generate each chunk? Have the authors attempted ablations on this hyperparameter?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "The paper is very well written, easy to follow and understand. The motivation is clearly explained and seems warranted, and the proposed solutions are elegant and simple. \n\nVideo results presented by the authors seem consistent and high-quality. This is even more pronounced when comparing to baseline generations.", "weaknesses": "Would like to see more related work on camera-guided video generation - how have the issues in this specific use-case been addressed in previous work? \n\nIn addition, it would be helpful to show comparisons to other methods for video generation which are able to condition on future camera poses. One of the existing baselines is limited by its autoregressive structure, and the other is designed for a specific use-case (panoramas). \n\nTable 3: Seems like the explicit loop closing technique hurts frame-to-frame consistency in order to improve long-range consistency. Have the authors considered ways to mitigate this loss in performance?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)