Conditional Panoramic Image Generation via Masked Autoregressive Modeling
Authors: Chaoyang Wang
Xiangtai Li
Lu Qi
Xiaofan Lin
Jinbin Bai
Qianyu Zhou
Yunhai Tong
Venue: NeurIPS 2025 poster ()
Keywords: generative model
panoramic image
Primary area: applications

Abstract: Recent progress in panoramic image generation has underscored two critical limitations in existing approaches. First, most methods are built upon diffusion models, which are inherently ill-suited for equirectangular projection (ERP) panoramas due to the violation of the identically and independently distributed (i.i.d.) Gaussian noise assumption caused by their spherical mapping. Second, these methods often treat text-conditioned generation (text-to-panorama) and image-conditioned generation (panorama outpainting) as separate tasks, relying on distinct architectures and task-specific data. In this work, we propose a unified framework, Panoramic AutoRegressive model (PAR), which leverages masked autoregressive modeling to address these challenges. PAR avoids the i.i.d. assumption constraint and integrates text and image conditioning into a cohesive architecture, enabling seamless generation across tasks. To address the inherent discontinuity in existing generative models, we introduce circular padding to enhance spatial coherence and propose a consistency alignment strategy to improve the generation quality. Extensive experiments demonstrate competitive performance in text-to-image generation and panorama outpainting tasks while showcasing promising scalability and generalization capabilities.

Conditional Panoramic Image Generation via Masked Autoregressive Modeling
Chaoyang Wang'
Xiangtai Li!
Lu Qi
Xiaofan Lin
Jinbin Bai
Qianyu Zhou Yunhai Tong'
School of Intelligence Science and Technology. Peking University
2 Insta360 Research
& National University of Singapore "The University Tokyo cywang @ stu. pkw.pdu.cc.cc qqlu 19u199200 gmail.com
Text Panoramas (Indoor)
(Cutdoer)
The photo depicts spacious, oleganrly designed hedinom large bed, wooden beams on ceiling, and view of balcony thrugh arched windows.
The depicts grand entrance havarious white-walled building with arched docrways, staircase, and well-mainnained garden nrea.
Panorama Outpainting
Panocamic Image Editing
park with pond and buildings
yellow tree in the yard.
Figure I: Generated samples from Panoramic AutoRegressive (PAR) Model. PAR unifies several conditional panoramic image generation tasks, including sat-to-panorame panorama outpainting, and panoramic image editing.
Abstract
Recent progress in panoramic image generation has underscored two critical lim- itations in existing approaches. First, most methods are built upon diffusion models, which are inherently ill-suited for equirectangular projection (ERP) panora- mas due to the violation of the identically and independently distributed (i.i..) Gaussian noise assumption caused by their spherical mapping. Second, these methods often treat text-conditioned generation (text-to- coxttto-panoramaa and image- conditioned generation (panorama outpainting) as separate tasks, relying on dis- tinct architectures and task-specific data. In this work, we propose unified framework, Panoramic AutoRegressive model (PAR), which leverages masked autoregressive modeling to address these challenges. PAR avoids the i.i.d. as- sumption constraint and integrates text and image conditioning into cohesive architecture, enabling seamless generation across tasks. To address the inher- ent discontinuity existing generative models, we introduce circular padding
*Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

to enhance spatial coherence and propose a consistency alignment strategy to improve the generation quality. Extensive experiments demonstrate competitive performance in text-to-image generation and panorama outpainting tasks while showcasing promising scalability and generalization capabilities. Project Page: https://warg chaoyang. woinngggthan github. io/projjec/ppar
Introduction
Panoramic image generation[2, 12, 13, 18, 19] has recently emerged as a pivotal research direction in computer vision, driven by its capability to capture immersive 360-degree horizontal and 180-degree vertical fields of view (FoV). This unique visual representation closely mimics human panoramic perception, enabling transformative applications across VR/AR [34, 79], autonomous driving [44], visual navigation [6, 25], and robotics [45, 60].
Despite some progress in recent years, existing panorama generation methods [18, 56, 57, 74, 70, 27, 36, 62, 77, 65, 64, 76] predominantly rely diffusion-based base models and face two significant problems: task separation and inherent limitations of the modeling approach. First, different panoramic generation tasks are highly bound to specific foundation models. For instance, most eext-to-panorant models [18, 56, 57, 74, 70] are based on Stable Diffusion (SD) [48, 43). In contrast, panorama outpainting models [27, 36. 62] are fine-tuned on SD-inpainting variants, resulting in task-specific pipelines that lack flexibility. Second, diffusion- based models struggle to handle the unique characteristics of equirectangular projection (ERP) panoramas, which map spherical data onto a 2D plane, violating the i.i.d. Gaussian noise assumption.
In addition to these fundamental issues, existing methods also contain redundant modeling, which introduces unnecessary computational costs. This redundancy manifests in several ways: 1) Some approaches [29, 36] iteratively inpaint and warp local areas, leading to error accumulation over successive steps. 2) Others [74, 55] adopt a dual-branch structure, with global and local branches, where the local branches not directly used final output targets. These challenges underscore the need for straightforward, unified, and efficient paradigm for generating conditional panoramic images.
In this work, we explore a powerful yet less explored panoramic image generation paradigm to address the above limitations: the violation of the i.i.d. assumption and the lack of unification. Moreover, we allow the model to take text or images as input and control the generated content at any position on the canvas. As shown in Fig. 1, PAR is competent for cext-to-panoram,, panorama outpainting, and editing tasks. Notably, our PAR requires no task-specific data engineering- both conditions seamlessly integrated via single likelihood objective. In comparison, Omni [68] relies on meticulously designed joint training datasets to align heterogeneous tasks, inevitably introducing complexity and domain bias.
For this goal, inspired by masked autoregressive modeling (MAR) [30], we propose PAR. On the one hand, it is based on AR modeling, which avoids the constraints of i.i.d. assumptions. On the other hand, allows for generation in arbitrary order, overcoming the shortcoming of traditional raster-scan methods that are incapable of image-condition generation. This flexibility of PAR unifies conditional generation tasks panorama outpainting reduces to a subset of text-to- panorama synthesis, where existing image tokens serve as partially observed sequences. Building upon the MAR-based generative framework, we introduce two critical enhancements to address inherent limitations in standard architectures. First, conventional VAE-based compression often exhibits spatial bias during encoding-decodiing center regions benefit from contextual information across all directions, while peripheral pixels suffer from unidirectional receptive fields, contradicting the cyclic nature of ERP panoramas. To mitigate this, we propose dual-space circular padding, applying cyclic padding operations in both latent and pixel spaces. This ensures seamless feature propagation across ERP boundaries, aligning geometric and semantic continuity. Second, as the foundation model is pre-trained on massive perspective images, we devise a translation consistency loss. This loss explicitly regularizes the model to accommodate the prior of ERP panoramas by minimizing discrepancies between the model's outputs under shifted and original inputs. Ultimately, two innovations holistically reconcile the unique demands of 360-degree imagery with the flexibility of MAR modeling. Experiments demonstrate that PAR outperforms specialist models in the text- to to-panorama task, with 37.37 FID on the Matterport3D dataset. Moreover, on the outpainting task,

---

it has better generation quality and avoids the problem of repeated structure generation. Ablation studies demonstrate the model's scalability in terms of computational cost and parameters, as well as its generalization to out-of -it-o---issribution (OOD) data and other tasks, such as zero-shot image editing.
Our contribution be summarized follows: 1) We propose PAR, which fundamentally avoids the conflict between ERP and i.i.d. assumption raised by diffusion models, and also seamlessly integrates text and image-conditioned generation within single architecture. 2) To enforce better adaptation to the panorama characteristic, we propose specialized designs, including dual-space circular padding and translation consistency loss. 3) We evaluate our model on popular benchmarks, demonstrating competitive performance in text and image-come canage-cnniiiioal panoramic image generation tasks. The results also prove the scalability and generalization capabilities of our model.
2 Related Work
Conditional Panoramic Image Generation. There are two primary task paradigms in this area:
1) text-to-panorama (T2P), which synthesizes 360-degree scenes from textual descriptions, and 2) panorama outpainting (PO), which extends partial visual inputs to full panoramas. Given the success of diffusion models (DMs) [31, 32, 33, 43, 48, 51, 52] on generation tasks, such as image generation [48, 75, 23, 43, 42, 17], image editing [4, 37, 49], image super resolution [22, 50, 71], video generation [3, 20, 69]. current approaches typically build upon DMs, yet face two problems. First, DM is inherently unsuitable for panorama tasks due to reliance on the i.i.d. Gaussian noise assumption, which ERP transformations violate. Second, existing approaches lack task unification. Specifically, T2P methods typically fine-tune SD, while PO solutions adapt the SD-inpainting variants. Although some work [68] attempts a unified solution, it is based on DM and relies on complex task- specific data engineering. From a methodological perspective, existing methods can be categorized into two main approaches: bottom-up and top-down. The former generates images with limited FoV sequentially [29, 36] simultaneously [57, 27] and finally stitches them together, but faces the problem of lacking global information and eгror accumulation. The latter [74, 55] typically employs a dual-branch architecture comprising panorama branch and a perspective branch. However, this design brings unnecessary computational cost, In parallel, several AR-based attempts have been made. They either rely on large DMs for final generation [36, 19], are limited to low resolution with additional up-sampling modules [10), or stitch perspective images without correct panorama geometry [80]. Overall, existing methods do not provide unified and theoretically sound solution for conditional panorama generation, which we rethink through MAR modeling.
Autoregressive Modeling. Motivated by the success of large language models (LLMM)) [5, 58), AR models, with great scalability and generalizability, are becoming a strong challenger to diffusion models. Several studies delve into visual AR with two distinct pipelines. One pipeline adopts raster-scan modeling [53, 47, 15, 16, 72, 63, 67, 28, 391, which treats the image as visual sentences and sequentially predicts the next token. However, this strategy does not fully exploit the spatial relationships of images. To make up for the shortcomings of raster-scan modeling, some works [7, 24, 73, 59, 30, 14, 1] extend the concept of masked generative models [8] to AR modeling, namely MAR. Instead of predicting one token within one forward, the model randomly selects several candidate positions and predicts attending to the decoded positions. Intuitively, this modeling provides flexible control over arbitrary spatial positions simply by adjusting the order of tokens to be decoded. Hence, we resort to MAR as flexible way to unify text and image-conditioned panorama generation while avoiding the theoretical defects of panorama DMs.
3 Method
This section is organized into three parts. First, we introduce the preliminaries of panorama projection and review the standard conditional AR modeling. Second, we analyze the disadvantages of this approach for unified conditional generation. Last, we describe our PAR in the following sections, including vanilla MAR architecture, consistency alignment, and circular padding.
3.1 Preliminaries
Equirectangular Projection is common parameterization for panoramic images. establishes bijection between spherical coordinates (0, σ) and 2D image coordinates (u, v). where 0 [0, m]
3

---

denotes the polar angle (latitude) and Φ (-T,, π] represents the azimuthal angle (longitude). This projection preserves complete spherical visual information while maintaining compatibility with conventional 21) convolutional operations. Given 3D point P = (x,, y, z) on the unit sphere $2, we first compute its spherical coordinates and then obtain ERP coordinates (u, v) via linear mapping:
2+
0
arctan
0 arccos(2),
W
H π
2x
where W H denotes the target image resolution.
Conditional Autoregressive Models formulate conditional data generation as a sequential process through next token prediction. Given an ordered sequence of tokens {ax ( the joint distribution p(z|c) factorizes into a product of conditional probabilities:
T
p(xlc) = II p( (eet,
where T<t = (I1, 11) denotes the historical context and indicates the conditions. This chain rule decomposition enables tractable maximum likelihood estimation, with the model parameters optimized to maximize:
T
(()) = E
Σ
log togpo (2, (x
(3)
where D represents the data distribution.
3.2 Rethinking Conditional Panoramic Image Generation
While DMs have demonstrated remarkable success in various image generation tasks, they rely on i.i.d. Gaussian noise assumption, which is violated by spatial distortions introduced by the ERP transformation in Eq. 1, making diffusion-baset methods inherently unsuitable for modeling panoramas. this work, we therefore rethink conditional panorama generation from AR modeling, which is not constrained by such assumptions.
Building upon the standard conditional AR formulation in Eq. 2, we analyze text-to-panorama and panorama outpainting scenarios. The former takes text embeddings while the latter takes observed image patches and optional textual prompts as conditions. In particular, this reformulates the two problems as follows:
p(xlc)= II p(a.laso) c), S(1) S (1, LES
where Sy denotes known token positions from the condition and S, = T} \ Sy. are targets. The context set S(t) dynamically aggregates:
8(1) (ili E Sa) < S.),
condition
generated content
This formulation generalizes pat-trm-paormmaa (S, ()) and outpainting (S& * i) scenarios. How- ever, traditional raster-scan modeling is not competent for the panorama outpainting task, as S₁ is not necessarily the beginning of the sequence.
3.3 Masked Autoregressive Modeling with Consistency Alignment
We aim to learn a generalized AR model that naturally unifies text and image conditions and adapts to panoramic characteristics. An intuitive solution is MAR modeling, which allows generation in arbitrary order by marking the position of each token. However, existing foundation models are typically pre-trained perspective images and cannot cope with panoramic scenarios. We delve into this problem and use the cyclic translation equivariance of ERP panoramas.
Vanilla Architecture. We start with vanilla text-conditioned MAR model [14] to learn basic image generation without special constraints. Specifically, we use continuous tokens instead of discrete ones to reduce quantization error.

---

Jp
[ mm
D
loss
loss
To
MLP Eg
MLP Eg
MLP Eg
t
M
Transformer f
Transformer f
Transformer f
C
Tp
3
X
(a) Vamilla MAR Training
(b) Cyclic Translation Consisteney
Figure Method illustration. (a) PAR utilizes a transformer f to predict regions obscured by mask M, then employs these predictions conditions to drive MLP in generating continuous tokens. The VAE encoder and decoder D are frozen. The dashed line indicates the inference phase. and denote textual embeddings and time-steps. respectively. (b) Both original and T Tu-aummented triples (x,, e, .)) are processed by the same model, and then aligned through consistency loss.
As illustrated in Fig. 2 (a), panoramic images are first compressed into latent representations using a VAE encoder E. These representations are fed into transformer model f. which adopts an encoder-decoder architecture. Within the encoder, x is divided into sequence of visual tokens via patchification. A subset of these tokens is randomly masked by replacing the corresponding regions with a designated [MASK] token. Sine-cosine positional encodings (PEs) are added to the token sequence to preserve spatial information.
Simultaneously, textual prompts are processed by pre-trained text encoder [26), which encodes them into textual embeddings c E Rd, where matches the dimensionality of the visual tokens. The unmasked visual tokens fused with the textual embeddings in the encoder, and the resulting contextualized representations are then passed to the decoder, where they are integrated with the masked tokens facilitate predictive reconstruction.
Unlike the standard AR modeling, the decoder's output not used as the final result, but as a conditional signal to drive lightweight denoising network, which is implemented by multilayer perceptron (MLP) Eg. During the training phase, the norse-corrupted na denoised by ég under 2 as follows:
Lra = Rr, M ||q co( =)
(6)
In Eq. 6, is pixel-wise multiplication with binary mask M, where the value corresponding to the and co are jointly optimized.
masked token is 1. The
Cyclic Translation Consistency. Current foundation models [48, 14] are predominantly trained on perspective images, leading to suboptimal feature representations for ERP-formateed panoramas. We introduce a cyclic translation operator T, to address this domain gap and construct semantically equivalent panorama pairs, where indicates arbitrary translation distance. As shown in Fig. (b), we construct an input pair, (I, e, M) and (z', M') = (T,(2),, (E), To (MM)). Both inputs share identical PE maps to prevent trivial solutions. The main model processes these pairs produce outputs and y' respectively. We enforce equivariance by aligning T, (v) with y only on masked regions through the objective:
Coonsisteney M' (ee, c
(7)
The final learning objective can be written as follows:
L Lea Xcconsistency:
(8)
where X is set 0.1 in experiments.
Discussion. The equivariance constraint holds exclusively for ERP panoramas due to their inherent horizontal continuity. For perspective images, applying T, artificially creates discontinuous bound- aries the center of the shifted image, which violates two critical premises. 1) Semantic equivalence. Unlike panoramas, shifted perspective images lose semantic consistency due to non-periodic scene
5

---

Tex22 Light
PanoL Jama
Pan Fusion
PAR (ours)
The photo shows panoramic modern, well-organized walk in closet with various shelves, hanging rods, and a large wardrobe door.
The image depicts spacious laundry room with cabinetry, white countertop, and red front-loeding washing and machines, surrounded by beige tiled floor and celling.
The photo shows beautiful outdoor staircase curved wall, leading residential area surrounded by trees and clear blue
The photo shows spacious, well- room pool table, large painting of red truck, and various furnishings, including a wooden cabinet and TV mounted the wall.
Figure 3: Visual comparisons with previous methods text-to-panoramn task. Previous methods neglect the circular consistency characteristics (yellow box), or suffer from repetitive objects (red box), artifact generation (blue box), and inconsistent combinations (green box). The highlighted portions in captions indicate text-image alignment failure cases of baseline methods.
structures. 2) Model prior. Generation models inherently enforce feature continuity in image interiors, making it infeasible to produce fragmented content.
Circular Padding. While the AR model incorporates panoramic priors, the VAE's latent space processing may still introduce discontinuities. Standard convolution operations in VAE provide sufficient context for central regions but suffer from information asymmetry at image edges pixels near boundaries receive unidirectional receptive fields. To address this issue, propose dual-space circular padding scheme: pre- pre-padding and post-padding. The former is applied in pixel space for the VAE encoder, while the latter is used in latent space for the VAE decoder. Specifically, crop particular portion of the left and right regions and concatenate them to the other side, as formulated in Eq. 9.
(x) = (of.at -rW/2 :], x(..,; r r\/2)).
where H and (x: E IR (x indicate the original and the padded tensor, respectively.
r indicates the ratio of padding width to that of the original images or latent variables. After the VAE transformation, the edge positions have sufficient context information. Therefore, the areas that belong to the padding are discarded.
4 Experiment
4.1 Experiment Settings
Implementation Details. We utilize NOVA [14] the initialization and set the resolution as 512 x 1024. Our model is trained for 20K iterations with a batch size 32. We employ an AdamW optimizer [35]. The learning rate is 5 x 10 with the linear scheduler. In the inference stage, set the CFG [23] coefficient as 5. this paper, the masking sequence for training and the sampling sequence for inference initialized with a uniform distribution unless otherwise stated.
Datasets and Metrics. We mainly use Matterport31) [6] for comparisons. The split the training and validation set follows PanFusion [74]. We use Janus-Pro-7B [9] to generate the captions.

---

Table 1: Performance text-to- pattoo-apaaaaa task. PO and PE represent panorama outpainting and
editing, respectively. Bold and underline indicate the first and second best entries.
Modeling Method
pparams
T2P
PO
PE
FAED
FID 4
CLIP Score
DS 4
Diffusion
PanFusion [74]
1.9B
5.12
45.21
31.07
0.71
Text2Light [10]
0.8B
68.90
70.42
27.90
7.55
Pano [80]
0.8B
33.15
103.51
32.54
13.99
AR
PAR (ours)
0.3B
3.39
41.15
30.21
0.58
PAR (ours)
0.6B
V
3.34
39.31
30.34
0.57
PAR (ours)
V
3.75
37.37
30.41
0.58
(4) Mask
(b) AOG-Net
(6) 2S-ODIS
(d) PAR w/o prompt
(e) PAR w/ prompt
Figure Qualitative comparisons of panorama outpainting on the Matterport3D dataset. PAR-1.4BB is used for this task, where PAR w/o prompt means the textual prompt is set as empty.
Several metrics used in this paper. Fréchet Inception Distance (FID) [21] measures the similarity between the distribution of real and generated images in a feature space derived from pre-trained inception network. Lower FID scores indicate better realism of synthesized results. CLIP Score (CS) [46] evaluates the alignment between text and image. Furthermore, as FID relies on an Inception network [54] trained on perspective images, we also report the Fréchet Auto-Encoder Distance (FAED) [41] score, which is a variant of FID customized for panorama. To measure the cycle consistency of panoramic images, we adopt Discontinuity Score (DS) [11].
Baselines. Since only few approaches combine T2P and PO, we compare PAR with specialist methods separately. Specifically, for T2P, compare with several diffusion-based and AR-based methods, including PanFusion [74), Text2Light [10], and PanoLllma [80]. We take AOG-Net [36] and 2S-ODIS [38] PO baselines. All experiments implemented the Matterport3D dataset.
4.2 Main Results
Tab. shows the quantitative comparison results. Within the scope of AR-based methods, our PAR significantly outperforms previous approaches regarding generation quality and continuity score. Our method is slightly inferior in the CLIP score metric, probably because CLIP [46] is pre-trained massive perspective images, and panoramic geometry is not well aligned with perspective images. Moreover, our model performs decently compared with strong diffusion-basee baseline, PanFusion, with only 0.3B parameters. Specifically, PAR-O.3B outperforms PanFusion with 1.73 and 4.06 on FAED and FID, with a discontinuity score of 0.58. Scaling up the parameters helps achieve better results, which is further illustrated in Sec. 4.3.
Fig. 3 provides visual comparisons with the aforementioned baseline methods, including diffusion- based and AR-based models. Our model shows better instruction-follwiii ability, more consistent generation results, and avoids duplicate generation. Specifically, Text2Light cannot obtain satisfactory synthesized results. PanoLlama cannot understand panorama geometry and neglects the characteristic of cycle consistency. PanFusion produces more reasonable results, but sometimes faces artifact problems. For example, the first and second columns of the third row show room without exit.
Panorama Outpainting. Tab. compares our method with AOG-Net and 2S-ODIS. We use the model trained with Eq. 8 and inference following Eq. 4, where Sş indicates the positions within mask. Our model achieves 32.68 and 12.20 on FID and FID-h, respectively. FID-h is similar to FID, but the difference is that it extracts eight perspective views in the horizontal direction with 90-degree FoV for each panoramic image, which pays more attention to the generation quality of local regions. Fig. 4 visualizes the synthesized results. Existing methods suffer from local distortion

---

Table 2: Panorama outpainting results the Matterport3D dataset. For FID-h, horizontally
sample 8 perspective images with FoV=90 for each panorama and then calculate their FID.
Method
T2P
PO
PE
FID
FID-h
AOG-Net [36]
V
83.02
37.88
2S-ODIS [38]
v
52.59
35.18
PAR w/o prompt
4
41.63
25.97
PAR w/ prompt
/
32.68
12.20
Scaling up truining compute
Scaling up model parameters
Figure 5: Scaling parameters and training compute improve fidelity and soundness. Two cases are shown with 3 model sizes and 3 different training stages. From top to bottom: 0.3B, 0.6B, 1.4B. From left right: 25%, 50%, 100% of the training process.
(the 3rd column) or counterfactual repetitive structures (the 2nd column). Our method overcomes these problems and still produces reasonable results without textual prompts.
4.3 Ablation Study
Scalability, We train with three different parameter sizes, namely O.3B, 0.6B, and 1.4B, respectively, In Tab. 1, FID and CS improve with increasing parameters. We also visualize the performance under different training stages, including 25%. 50%, and 100% of the whole training process. As shown in Fig. 5, the improvement of fidelity and soundness is consistent with the scaling of parameters and computation. Larger models learn data distributions better, such as details and panoramic geometry.
Generalization. Fig. 6 visualizes the outpainting results of PAR on OOD data. SUN360 [66] is used for evaluation, which has different data distributions from Matterport3D. PAR shows decent performance across various scenarios. We also compared with several PO basclines trained on the SUN360 dataset. However, Diffusion360 [18] suffers from unrealistic scenes and lacks details. Panodiff [62] also encounters artifact problems (red sky in the 1st row).
PAR can also adapt different tasks, such as panoramic image editing. As shown Fig. 7, the model needs to preserve the content inside the mask and regenerate according to new textual prompts. We designed three challenging text prompts, whose content differs from the training datasets, and contains specific conflicts with the original image. For example, fire yard in (b), snow current season scene in (c), and ocean vs. current place of the scene in (d). Interestingly, our model can generate them well and understands panoramic characteristics. As shown in Fig. 7 (b), the left and right sides of the image can be stitched to form whole fire.
Translation Equivariance. Tab. 3 demonstrates the effectiveness of consistency loss. Eq. 7 helps the model adapt to panoramic characteristics, thus improving generation quality. Fig. 8 (left) shows the convergence of r. Post food-ng signif- icantly improves DS when postt > as it helps to smooth stitching seams in the pixel space. However, the model without pre-padding suffers from higher discontinuity, indicating the misalignment between the training and testing phases. With
Table 3: Ablations cyclic consistency loss.
Method
FID
CLIP Score f
DS 4
w/o Loonsistency
39.55
30,25
0.57
w/ Lconststency
37.37
30.41
0.58

---

Mask
(b) Punodiff
Diflusion360
(d) PAR (ours)
Figure 6: Panorama outpainting on OOD dataset. The images are from SUN360, which is out of the distribution of our training data. PAR generates realistic panorama images while previous methods have problems like artifacts, or unrealistic results.
(3) Original
Bunes are blazing yard
sports cir pakked snow
(d) A house
Figure 7: Panoramic image editing. We design several challenging textual prompts to enforce the model regenerate the contents outside the red box while maintaining the inside contents. Note that our PAR performs zero-shot task generalization.
Fye
3.-
0.125
0.25
255
Fyre 0.5
0
1.0
05.
0.0
0.1
0.3
Fpre
0 Xpoot
Apre 0.5 Ppose
Fpro 0.5
Fpost
Fpost
Tpost
Figure 8: Left: The curve of DS about poptt- Right: Visualizations of different circular padding strategies. Each part consists of three images, where the top one represents the panoramic image generation result, the left and right ones represent the edge stitching results, respectively. The red box indicates the semantic discontinuity, and the green box highlights the seam after post-padding.
the same Tpre, DS converges quickly when Tpost > 0.125. A similar phenomenon is observed with Tpost fixed. Specifically, the curve Tpre = 0.25 and Tpre = 0.5 almost coincide.
Fig. 8 (right) visually explains the difference between the two padding strategies. When Tpre = 0, semantic inconsistency exists between the left and right edges of the panoramic image. Post-padding cannot repair it even with large padding ratio, and the green box bounds the smoothed seam. In contrast, the model generates consistent contents when pre- pre-padee pre-padding is applied.
Conclusion
This paper proposes PAR, a MAR-based architecture that unifies text- and image-conditioned panoramic image generation. We delve into the cyclic translation equivariance of ERP panora- mas and correspondingly propose a consistency alignment and circular padding strategy. Experiments demonstrate PAR's 's effectiveness text-to-panorama and panorama outpainting tasks, whilst showing

---

promising scalability and generalization capability. We hope our research provides new solution for the panorama generation community.
Acknowledgement. This work was supported by the National Key Research and Development Program of China (No. 2023YFC380760)..
References
[1] Jinbin Bai. Tian Ye. Wei Chow, Enxin Song. Qing-Guo Chen, Xiangtai Li, Zhen Dong, Lei Zhu, and
Shuicheng Yan. Meissonie: Revitalizing masked generative transformers for efficient high-resolution text-to-image synthesis, arXiv preprint mxiv:2410.08226 2024. 3
[2] Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. Multidiffusion: Fusing diffusion paths for
controlled image generation. In JCML. 2023. 2
[3] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz,
Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint orXiv:2311.15127 2023. 3
[4] Tim Brooks, Aleksander Holynski, and Alexci A Efros. Instructpix2pix: Learning to follow image editing instructions. CVPR, 2023. 3
[5] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert- Voss, Gretchen Krueger. Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse. Mark Chen, Eric Sigler. Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Nlya Sutskever, and Dario Amodei. Language models are few-shot learners. In NeurlPS, 2020. 3
[6] Anell Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3d: Learning from rgb-d data in indoor environments. arXiv preprint arXiv: / 7009 770.00 058 2017. 2, 6
[7] Huiwen Chang. Han Zhang. Jarred Barber, AJ Maschinot, Jose Lezama, Lu Jiang. Ming-Hsuan Yang. Kevin Murphy, William T Freeman. Michael Rubinstein, et al. Muse: Text-to-image generation via masked generative transformers. arXiv preprint arXiv:2301.0070 2023. 3
[8] Huiwen Chang. Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. Maskgit: Masked generative image
transformer. In CVPR, 2022. 3
[9] Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan. Janus- pro: Unitied multimodal understanding and generation with data and model scaling. arXiv
preprint arXiv:2501. 178/ 1, 2025. 6
[10] Zhaoxi Chen, Guangcong Wang, and Ziwei Liu. Text2light: Zero-shot text-driven hdr panorama generation.
ACM Transactions Graphics, 2022. 3.7
[11] Anders Christensen, Nooshin Mojab, Khushman Patel, Karan Ahuja, Zeynep Akata, Ole Winther, Mar Gonzalez-Franco, and Andrea Colaco. Geometry fidelity for spherical images. In ECCV, 2024. 7
[12] Thiago LT da Silveira, Paulo GL Pinto, Jeftri Baaruugrra-Llerenne and Cláudio R Jung. 3d seene geometry estimation from 360 imagery: A survey. ACM Computing Surveys, 2022. 2
[13] Mohammad Reza Karimi Dastjerdi, Yannick Hold- hold-Geeffroy, Jonathan Eisenmann, Siavash Khodadadeh, and Jean-François Lalonde. Guided co-modulated gan for 360 field of view extrapolation. In 3DV, 2022. 2
[14] Haoge Deng, Ting Pan, Haiwen Diao, Zhengxiong Luo, Yufeng Cui, Huchuan Lu, Shiguang Shan, Yonggang Qi, and Xinlong Wang. Autoregressive video generation without vector quantization. arXiv preprint arXiv:2412. 14169, 2024. 3, 6
[15] Ming Ding. Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, et al. Cogview: Mastering text-to- ext-to-imaae generation via transformers. In NeurlPS, 2021. 3
[16] Ming Ding. Wendi Zheng, Wenyi Hong, and Jie Tang. Cogview2: Faster and better text-to-image generation
via hierarchical transformers. In NeurlPS, 2022. 3
[17] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, al. Scaling rectified flow transformers for high-resolution image synthesis. In JCML, 2024. 3
[18] Mengyang Feng, Jinlin Liu, Miaomiao Cui, and Xuansong Xic. Diffusion360: Seamless 360 degree panoramic image generation based on diffusion models. arXiv preprint arXiv:231 1.3314,, 2023. 2, 8, 25
[19] Penglei Gao, Kai Yao, Tiandi Ye. Steven Wang, Yuan Yao, and Xiaofeng Wang. Opa-ma: Text guided mamba for 360-degree image painttin arXiv preprint arXiv:2407.1092, 2024. 2.
[20] William Harvey, Sacid Naderiparizi, Vaden Masrani, Christian Weilhach, and Frank Wood. Flexible diffusion modeling of long videos. In NeurlPS, 2022.
[21] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two mmaesccal update rule converge to local nash equilibrium. In NewrIPS, 2017. 7
10

---

[22] Jonathan Ho. Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation. The Journal of Machine Learning Research,
2022. 3
[23] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NeurlPS Workshops, 2021. 3, 6
[24] Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, and Jie Tang. Cogvideo: Large-scale pretraining for text-to-video generation via transformers. arXiv preprint arXiv:2205. 15868, 2022. 3
[25] Kuan-Chih Huang, Xiangtai Li, Lu Qi, Shuicheng Yan, and Ming-Hsuan Yang. Reason3d: Searching and reasoning 3d segmentation via large language model. In 3DV, 2025. 2
[26] Mojan Javaheripi. Sébastien Bubeck, Marah Abdin, Jyoti Aneja, Sebastien Bubeck, Caio César Teodoro Mendes, Weizhu Chen, Allie Del Giorn, Ronen Eldan, Sivakanth Gopi, et al. Phi-2: The surprising power of small language models. Microsoft Research Blog, 2023. 5
[27] Nikolai Kalischek, Michael Oechsle, Fabian Manhardt, Philipp Henzler, Konrad Schindler, and Federico Tombari. Cubediff: Repurposing diffusion-based image models for panorama generation. arXiv preprint
arXiv:2501.71162, 2025. 2,3
[28] Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang, Rachel Hornung, Hartwig Adam, Hassan Akbari, Yair Alon, Vighnesh Birodkar, et al. Videopoet: A large language model for zero-shot video generation. arXiv preprint arXiv:2312.14125, 2023. 3
[29] Jialu Li and Mohit Bansal. Panogen: Text-conditioned panoramic environment generation for vision-and- language navigation. In NeurlPS, 2023. 2,3
[30] Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation
without vector quantization. In NeurlPS, 2024. 2,
[31] Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Mait Le. Flow matching for generative modeling. In ICLR, 2023. 3
[32] Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. In ICLR, 2023. 3
[33] Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, et al. Instaflow: One step is enough for high-quality diffusion- based text-to-image generation. In ICLR, 2024. 3
[34] Yuheng Liu, Xinke Li, Xueting Li, Lu Qi, Chongshou Li, and Ming- Hisng Yang. Pyramid diffusion for fine 3d large scene generation. In ECCV, 2024. 2
[35] Ilya Loshchilov, Frank Hutter, et al. Fixing weight decay regularization in adam. arXiv preprint 10X00 2017. 6
[36] Zhuqiang Lu, Kun Hu, Chaoyue Wang, Lei Bai, and Zhiyong Wang. Autoregressive omni-aware outpainting for open-vocabulary 360-degree image generation. In AAAI, 2024. 2, 3, 7,8
[37] Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool.
Repaint: Inpainting using denoising diffusion probabilistic models. In CVPR, 2022. 3
[38] Atsuya Nakata and Takao Yamanaka. 2s-odis: Two-stage oini-diraational image synthesis by geometric
distortion correction. In ECCV, 2024. 7,8
[39] Charlie Nash, Joao Carreira, Jacoh Walker, Tain Barr, Andrew Jaegle, Mateusz Malinowski, and Peter Battaglia. Transframer: Arbitrary frame prediction with generative models. arXiv preprint
urXiv:2203.088 02290449944,.2 2022. 3
[40] Jinhong Ni, Chang-Bini Zhang, Qiang Zhang, and Jing Zhang. What makes for text to 360-degree panorama generation with stable diffusion? ICCV, 2025. 23
[41] Changgyoon Oh, Wonjune Cho, Yujeong Chae, Dachee Park, Lin Wang, and Kuk-Jin Yoon. Bips: Bi-modal
indoor panorama synthesis via residual depth-aided adversarial learning. In ECCV, 2022. 7
[42] William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, 2023.
[43] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. In ICLR, 2024. 2.
[44] Lu Qi. Li Jiang, Shu Liu, Xiaoyong Shen, and Jiaya Jia. Amodal instance segmentation with kins dataset.
In CVPR, 2019. 2
[45] Yu Qiu, Xin Lin, Jingbo Wang, Xiangtai Li. Lu Qi, and Ming-Hsuan Yang. Ume: Unified resilient controller for legged robots with joint malfunctions. In arXiv, 2025.
[46] Alec Radford, Jong Wook Kim, Chris Hallacy. Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, al. Learning transferable visual models from natural language supervision. In ICML, 2021.7 7
[47] Aditya Ramesh, Mikhail Pavlow, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In ICML, 2021. 3
[48] Robin Rombach, Andreas Blattmann. Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent difffusion models. In CVPR, 2022. 3.5
[49] Chitwan Saharia. William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-imann to-image diffusion models. In SIGGRAPH, 2022. 3
11

---

[50]
Chitwan Saharia. Jonathan Ho. William Chan, Tim Salimans. David J Fleet, and Mohammad Norouzi. Image super-resolution via iterative refinement. TPAMI, 2022. 3
[51]
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, 2021. 3 Yang Song, Jascha Sohl- Sill-Dicsttei, Diederik Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In ICLR, 2021.
[52]
[53] Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang. Bingyue Peng. Ping Luo, and Zehuan Yuan. Autore- gressive model beats diffuusin: Llama for scalable image generation. arXiv preprint arXiv:2406.06522
2024. 3
[54] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zhigniew Wojna. Rethinking the inception architecture for computer vision. CVPR, 2016.
[55] Jing Tan, Shuai Yang. Tong Wu, Jingwen He, Yuwei Guo, Ziwei Liu, and Dahua Lin. Imagine360: Immersive 360 video generation from perspective anchor. arXiv preprint arXiv:44/2.35522 2024. 2, [56] Shitao Tang. Jiacheng Chen, Dilin Wang, Chengzhou Tang. Fuyang Zhang. Yuchen Fan, Vikas Chandra, Yasutaka Furukawa, and Rakesh Ranjan. Mydiffusion++:: A dense high-resolution multi-view diffusion model for single or sparse-viet view 3d object reconstruction. In ECCV, 2024. 2
[57] Shitao Tang, Fuyang Zhang, Jiacheng Chen, Peng Wang, and Yasutaka Furukawa. Mvdiffusion: Enabling holistic multi-view image generation with correspondence-wwarcc diffusion. In NeurlPS, 2023. 2,
[58] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv: 2307.09288, 2023. 3
[59] Ruben Villegas, Mohammad Babacizadeh, Picter-Jan Kindermans, Hernan Moraldo, Han Zhang, Moham- mad Taghi Saffar, Santiago Castro, Julius Kunze, and Dumitru Erhan. Phenaki: Variable length video generation from open domain textual descriptions. In ICLR, 2022. 3
[60] Zhaoliang Wan, Yonggen Ling, Senlin Yi. Lu Qi, Wangwei Lee, Minglei Lu, Sicheng Yang, Xiao Teng, Peng Lu, Xu Yang, al. Vint-6d: A large-scale object-in-hand dataset from vision, touch and proprioception. In JCML, 2024.
[61] Hai Wang, Xiaoyu Xiang, Yuchen Fan, and Jing-Hao Xue. Customizing 360-degree panoramas through text-to-inen -maag diffusion models. In WACV, 2024. 23
[62] Jionghao Wang. Ziyu Chen, Jun Ling, Rong Xie, and Li Song. 360-degree panorama generation from few unregistered nfov images. arXiv preprint arXiv:2308. 14686, 2023. 2, 8, 24
[63] Yuqing Wang, Tianwei Xiong, Daquan Zhou, Zhijie Lin, Yang Zhao, Bingyi Kang. Jiashi Feng, and Xihui Liu. Loong: Generating minute-level long videos with autoregressive language models. arXiv preprint arXiv:2410.027577 2024. 3
[64] Tao Wu, Xuewei Li, Zhongang Qi, Di Hu, Xintao Wang, Ying Shan, and Xi Li. Spherediffusion: Spherical
geometry-awan aware distortion resilient diffusion model. AAAI, 2024. 2
[65] Tianhao Wu, Chuanxia Zheng, and Tat-Jen Cham. Panodiffusion: 360-degree panorama outpainting via
diffusion. arXiv preprini arXiv:2307.03)
2023. 2
[66]
Jianxiong Xiao, Krista A Ehinger, Aude Oliva, and Antonio Torralba. Recognizing seene viewpoint using panoramic place representation. In CVPR, 2012. 8
[67] Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. Videogpt: Video generation using vq-vae and transformers. arXiv preprint arXiv:2104. (0/55, 2021.
[68] Liu Yang. Huiyu Duan. Yucheng Zhu, Xiaohong Liu, Lu Liu, Zitong Xu, Guangji Ma, Xiongkuo Min, Guangtao Zhai, and Patrick Le Callet. Omni 2: Unifying omnidirectional image generation and editing in an omni model. arXiv preprint arXiv:2504.1 1144.115 1179,, 2025. 2, 3
[69] Ruihan Yang, Prakhar Srivastava, and Stephan Mandt. Diffusion prohabilistic modeling for video genera-
tion. Entropy, 2023. 3
[70] Weicai Ye, Chenhao Ji. Zheng Chen, Junyao Gao, Xiaoshui Huang, Song- Zhang, Wanli Ouyang. Tong He, Cairong Zhao, and Guofeng Zhang. Diffpano: Scalable and consistent text to panorama generation with spherical epipolar-aware diffusion. arXiv preprint arXiv:2410.2423 242003, 2024. 23
[71] Fanghua Yu, Jinjin Gu. Zheyuan Li, Jinfan Hu. Xiangtao Kong, Xintao Wang. Jingwen He, Yu Qiao, and Chao Dong Scaling up excellence: Practicing model scaling for photo-realistic image restoration in the wild. In CVPR, 2024. 3
[72] Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang. Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, al. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.1013 10789, 2022.
[73] Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G Hauptmann, Ming-Hsuan Yang. Yuan Hao, Irfan Essa, et al. Magvit: Masked generative video transformer. In CVPR,
2023. 3
[74] Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella, Xiaoshui Huang, Dinh Phung, Wanli Ouyang, and Jianfei Cai. Taming stable diffusion for text to 360 panorama image generation. In CVPR, 2024. 2, 3,3, 6,
[75] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to- text-to-image diffusson models. In ICCV, 2023.
12

---

[76]
Xiaoyu Zhang, Teng Zhou, Xinlong Zhang, Jia Wei, and Yongchuan Tang. Multi-scale diffusion: Enhancing
spatial layout in high-resolution panoramic image generation. arXiv preprini arXiv:2410.1883 2024.
[77] Dian Zheng, Cheng Zhang. Xiao-Ming Wu, Cao Li, Chengfei Lv, Jian-Fang Hu, and Wei-Shi Zheng.
Panorama generation from nfov image done right. arXiv preprint arXiv:2503. 18420, 2025. 2
[78] Zheng, Junfei Zhang. Jing Rui Tang. Shenghua Gao, and Zihan Zhou. Structured3d:
large
photo-realistic dataset for structured 3d modeling. In ECCV, 2020. 23
[79] Junwei Zhou, Xueting Li, Lu Qi, and Ming- mang- Yang. Layout-your-3d Controllable and precise 3d generation with 2d blueprint. ICLR, 2025.
[80] Teng Zhou, Xiaoyu Zhang, and Yongchuan Tang. Panollama: Generating endless and coherent panoramas pext-token-prediciion
with next-token prediction Ilms. arXiv preprint urXiv:2411.15867 2024. 3, 25
13

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
We include the main claims in the abstract and introduction.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how be expected to generalize other settings.
much the results
is fine to include aspirational goals as motivation as long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
We discuss the limitations in the appendix.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification. asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low images are taken in low lighting. Or speech-to-text system might be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach address problems of privacy and faimess.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question:
For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
14

---

Justification:
The proof is in the appendix.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification:
We provide details the implementation in Sec. 4 and appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make clear how to reproduce that algorithm. (b) the contribution is primarily a new model architecture, the paper should describe the architccture clearly and fully.
(c) If the contribution is new model (e.g., a large language model), then there should either way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reprodueibility. the case of closed-source models, may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
15

---

Answer:
[Yes]
Justification:
The code is released.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.. cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https:
/ //nips. wttpccccpppccccceeececceccccccct httpa//gggiieee/eeeuuuuiiiiiiiii /CodeSubmic ssionnollll onPolicy))) for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: Experimental setting and details are in Sec. 4. Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [No]
Justification:
We did not conduct experiments with error bars.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, statistical significance tests, at least for the experiments that support main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
should be clear whether the error bar is the standard deviation the standard error of the mean.
16

---

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of enrors is not verified.
For asymmetric distributions, the authors should be careful to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of exccution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
We provide details of computation cost in the appendix.
Guidelines:
The answer NA means that the paper does include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, cloud provider, including relevant memory and storage.
The paper should provide the amount compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https: //neurrris..../ cc wwtppblcc//aacccccccccccccccccct CthicsGuieelines"
Answer: [Yes]
Justification: We conducted the paper conform, in every respect, with the NeurIPS Code of Ethics
Guidelines:
The answer NA means that the authors have not reviewed the NeurlPS Code of Ethics.
If the authors answer No, they should explain the special cireumstances that require deviation from the Code of Ethics.
The authors should make to preserve anonymity (e.g.. if there is special consid- cration due to laws or regulations their jurisdiction..
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification:
We discuss the broader impact in the appendix.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
17

---

The conference expects that many papers will be foundational research and not tied particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility of MI..
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [Yes]
Justification:
We discuss the broader impact in the appendix.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Lieenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification:
We use existing assets with properly credited.
Guidelines:
The answer NA means that the paper does not existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com com/dataseta has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
18

---

If this information is not available online, the authors are encouraged to reach out to the asset's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: The paper does not release new assets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/mmoel as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question:
Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such the institution conducting the review.
16. Declaration of LLM usage
19

---

Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, or originality the research, declaration is not required.
Answer: [NA]
Justification: The core method development in this research does not involve LLMs any important, original, or non-standard components.
Guidelines:
The answer NA
that the method development in this research does involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy (https : h/tp://ies...//eeeeeeeeee http///rrs..c//ooeeeeeeeeeeeeeee / for what should or should be described.
20

---

Overview. In this appendix, we present more results and details:
A. Proof of non-i..d. of ERP transformation.
B. More details on method.
More empirical studies and visual results.
D. Discussion of the broader impacts, safeguards and limitations.
A Proof of Non-ii.. of ERP Transformation
A.1 Problem Setting
Let the unit sphere
x² +2 + +2² =1} 8² = (1r, E E
be parameterised by spherical angles (Ø, 0) (-,, x 10, x|. The equirectangular projection of spatial resolution (H, W) N² is the mapping
W) x H) (-
x 00, T)
(u, v) + (d(u),
with
Φ(u)
2
n
(
n
W
H
2+ W,
0 H.
Conversely,
2x
A.2
Gaussian noise field on the sphere
Let {=( 0) [ be Gaussian noise field on the sphere, i.e.
E[=(o, 0)] 0,
C) 8)) و² 8(Φ 8(0
(10)
where 5 is the Dirac distribution and o² 0 is constant variance density (per steradian).
A.3 Pixel value definition in ERP
Each ERP pixel (u, v) E {0.... ....... W - 1} x {0, (O....,H. H. 1} covers spherical patch
Pun [b(u- - })) vlu 1) x (o(o- - -10 + 1)
Its area is
2x
2x²
Auc with the mid-latitude 0, = A(v) = H. The noise value stored in the pixel is the area-average
sin A do do
ccos A(v 2) cos 0(v + 2)]
sin P.,
(11)
Put:
W
WH
Tae =
e) 0) dA, dA do d0.
(12)
Ans Pue
Because the integral in Eq. 12 is a linear functional of the Gaussian field s, each Nue is still Gaussian.
A.4 Independence
For two different pixels (u, v) # (u', v') the patches Pav: Purer disjoint. Since Eq. 10 states that E(Ø, 0) is white, integrals over disjoint regions are uncorrelated, hence Gaussian independent:
o² 220 6')o(0 6¹) dAdA' =0 o Tue IL Tu'
Cov(Tue:Thet Th P... Pale
(13)
21

---

A.5 Non-identical distribution
The variance of one pixel, using Eq. 10, Eq. 11, and Eq. 12, is
Var(Hae) = A /1 /// Puo
0² Aur
WH
0² 6')00000(00 - (0:00-0'ddAA d''
=
(14)
2TT²
sin 0g
Eq. 14 depends the latitude index through the factor sin 0g. Pixels close to the poles (0, 2 0 or T) possess larger variance than pixels near the equator (0, = 7/2). Hence
Var(Huw) * Var(Na,er) for + v'.
B More Details on Method
Implementation Details. The sampling step for PAR is set to 64 and r = 0.125 by default. We use Janus Pro-7B to get the captions with the template "Use one sentence describe the photo in detail.". We then calculate the token lengths and keep them less than 77. Experiments shows that less than 0.1% of the captions not meet the requirements, and we add "and briefty" at the end of those prompts.
Framework Details. The transformer takes patchified visual tokens (from the VAE encoder), masking indicators, and text embeddings as inputs. outputs conditional signal to drive the subsequent denoising network MLP. The decoding mechanism is illustrated in Fig. 9.
The MLP has multiple stacked blocks. Within each block, the input is first processed by AdaLNZero to get gating coefficients. It is then transformed using projection layer consisting of two linear layers with silu activation. The result is then .N-oormalized and multiplied by the gating coefficients, and finally combined with residual connection.
Image (Generated)
Image token be generated step)
Figure 9: Illustration of decoding mechanism.
C More Empirical Studies and Visual Results
Time Analysis. takes about days to train the 1.4B model on NVIDIA A100 GPUs. As shown in Tab. 4, we benchmark the inference speed for the 0.3B model with different AR steps using one NVIDIA A100 GPU with a batch size 8. The denoising steps for MI.P are 25.
Table 4: Ablations of inference time under different AR steps.
AR steps
16
32
64
Inference speed (sec/image) 4
3.02
5.49
10.03
We also compare PAR-0.3B with PanFusion with 64 AR steps in Tab. 5.
Table 5: Comparison with PanFusion regarding inference time.
PanFusion
PAR (ours)
Inference speed (sec/image) 1
28.91
10.03
FID |
45.21
41.15
22

---

Table 6: Ablations of different padding ratios regarding inference time.
Padding Ratio
0
0.0625
0.125
0.25
0.5
Inference speed (sec/image) Additional costs
2.99
3.01
3.02
3.04
3.05
0
0.67%
1.00%
1.67%
2.01%
Table 7: next-to-panoramt results the Matterport3D dataset.
FAED 4
FID
CLIP Score f
DS 4
DintPPano [70]
10.03
53.29
30.31
6,16
UniPano [40]
5.87
44.74
30.45
0.77
PAR-0.3B (ours)
3,39
41.15
30.21
0.58
As shown in Tab. 6, circular padding does not bring about additional computational cost in the transformer and MLP, which account for the majority of inference time. The AR step is 16.
More Results on Text-to-Panorama. Tab. shows that our method has advantages on text-to- panorama task. We also provide more visual results in Fig. 10. Our model can generate reasonable panoramic images under the guidance of textual prompts. Tab. 9 provides ablation study for different denoising steps with our 0.3B model.
More Results Panorama Outpainting. Tab, shows that our method has advantages on panorama outpainting task. We provide more visual results for panorama outpainting in Fig. 11. We test the performance of our model in indoor setting and outdoor setting, and our model can achieve decent results, which proves its generalizability.
Visual Results of Circular Padding. Fig. 12 compares different circular padding strategies, including
Ypre = 0 and pre = 0.5. Tpost ranges from 0 to 0.5. It converges quickly with Tpost >> 0. fpre helps to maintain cycle consistency the semantic level.
Classifier-free Guidance. We evaluate PAR-I.4 with different CFG coefficients. The model obtains 40.04 FID when CFG=3 and 39.76 FID when CFG 10, indicating that too large too small guidance strength deteriorates the quality of the generation.
Ablations on other datasets. We include additional experiments using the Structured3D [78] dataset, which a large-scale synthesized indoor dataset for house design with well-preserved poles. We sampled 9000 images for training and 1000 for testing. In Tab. 10, our method outperforms Pano Fig. 14 visualizes the synthesized results of PAR-0.3B. This indicates that the blur the pole regions originates from the dataset and is irrelevant to the model, which can also be demonstrated by the visualizations of perspective projections in Fig. 13.
Zero-shot inference on OOD datasets. We evaluate panorama discontinuity using both our method and StifchDiffusion [61]. StitchDiffusion forces the model to fuse seam areas during inference. On the contrary, our pre-padding makes the model learn semantic consistency during the training phase, and post-padding achieves pixel-Ievel consistency in the inference stage. In this experiment, PAR- 0.3B and StitchDiffusion generate images for 100 text prompts sampled from subset of SUN360, respectively. Our method achieves DS score of 0.63, outperforming StitchDiffusion's 1.12.
For zero-shot outpainting, we compare the two methods on 100 images from the Structured3D dataset, which is excluded from both training sets for faimess. As shown in Tab. 11, our model outperforms Diffusion360.
Failure case. As shown in Fig. 15, our model may fail in some details of small objects, such as tables and sofas.
D Discussion
Comparisons to multi-view diffusion models. On the one hand, our AR-based method has several advantages over multi-view diffusion models (MVDMs). First, Computational Efficiency. MVDMs require generating several perspective images to cover panorama, resulting in more computational
23

---

The image depicts spacious, elegantly designed living with large windows, white walls, and mix of modern and traditional furniture, including sofas, coffee table, and grand piano.
The image depicts spacious, elegant kitchen and dining with round wooden table, white chairs, and large window adorned with curtains, showcasing blend of modern and classic design clements.
The image depicts spacious, well-lit living room with mounted TV, comfortable scating, and view of the kitchen and dining area the buckground.
The image depicts spacious, modemn bathroom with large bathtub, double vanity, and large windows allowing natural light to fill the room.
Figure 10:
Visualization of text-to- text-to-panorama panorama generation, including the generated results and corre- sponding textual prompts.
Table 8: Panorama outpainting results on the Matterport3D dataset.
FID 4
FID-h 4
PifoDiff [62]
65.11
46.58
PAR w/o prompt
41.63
25.97
cost due to the attention mechanism. Second, Global Consistency. Coordinating multiple views demands complex attention mechanisms to ensure coherence, and struggles to maintain a consistent global understanding- sometimes leading duplicated or inconsistent content. Third, Fewer Seam Issues. MVDMs must handle more seams the boundaries between views, which increases the difficulty of achieving seamless stitching and can introduce visible artifacts.
On the other hand, since our model directly generates the entire panoramic image, it needs to use panoramic data to drive the pre-trained model (trained on perspective images) to adapt to the panoramic domain knowledge, which might be less efficient.
Broader Impacts. Our proposed PAR employs autoregressive set-by-set modeling for panoramic image generation tasks. It has significant advantages in generation performance compared with previous methods. Moreover, it shows the capability of zero-shot task generalization, like panorama outpainting and editing. This an attempt towards the unification model the field panoramic image generation and will motivate people to explore the unification of different tasks in panorama generation. From the perspective of social impact, this will help the creation of artistic content, but the same time it may result in the problem of fake content.
Safeguards. The content generated by the generative model, including AR models in this paper, demonstrates high degree of correlation with the training data. From this perspective, is important effectively prevent the
ensure the fairness and cleanliness of the training data. In this way, model from generating harmful content.
Limitations. We find that there is still a gap between the generated results and the real panoramic images, such as some details and textures. We argue that scaling on more realistic images can help alleviate this problem. However, due to the scarcity of panoramic data, we leave it as future work.

---

Table 9: Ablations of denoising steps.
Steps
10
20
30
40
FID
41.57
40.19
41.98
43.77
Figure 11: Visualization of panorama outpainting results.
Tew - 0.5
Figure 12: Visualization results of circular padding under different settings.
Table 10: Comparisons of PAR-0.3B and Pano Structured3DD dataset.
FID
CLIP Score
DS
PanoLlama [80]
125.35
33.90 30.75
16.37
PAR-0.3B
47.02
0.68
Table 11: Zero-shot panorama outpainting results.
FID |
FID-h
Diffusion360 [18]
140.91
74.89
PAR w/o prompt
127.01
68.27
25

---

Panoramic Images
Perspective Images
Figure 13: Perspective projections of synthesized results on Matterport3D dataset.
image depicts modern, well-lit interior of home with kitchen, dining hallway, living and staircase, showcasing warm and inviting atmosphere.
The image depicts modern, well-organized bedroom with a large bed, wooden flooring, variety furniture including wardrobe, chair, desk, and dresser.
Figure 14: Visualizations of synthesized results on Structured3D dataset.
(a) The photo depicts spacious, modern bedroom with a large bed, sitting area, and baleony overlooking scenie view of the occan.
(b) The image depicts modern, spacious hotel room with two twin beds, sleek fumniture, and a clean, neutral color palette.
Figure 15: Failure cases.
26


Reviews:
[{"review_id": "IwxwQtuUha", "reviewer": "Reviewer_mEAi", "summary": "This paper proposes Panoramic AutoRegressive (PAR) model which is a unified framework for conditional panoramic image generation, addressing two limitations in existing approaches. The first one is the fundamental incompatibility between diffusion models and equirectangular projection panoramas, which violate the i.i.d. Gaussian noise assumption due to spherical distortions. The second is the unification of text-to-panorama and panorama outpainting tasks within a single autoregressive architecture, eliminating the need for task-specific models or data engineering. PAR leverages masked autoregressive modeling to enable flexible generation orders and integrates two novel components: dual-space circular padding which is applied in latent and pixel spaces to enforce spatial coherence across equirectangular projection boundaries, and a translation consistency loss to provide alignment of the model with panoramic spherical geometry priors. Experiments conducted on Matterport3D demonstrate competitive results (e.g., 37.37 FID for T2P) and strong generalization to out-of-distribution data (SUN360) and zero-shot editing tasks.", "questions": "The following actions may improve the paper\n1. Add failure cases analysis\n2. Add comparison with multitask model Omni^2\n3. Conduct computational cost estimation experiments to evaluate training/inference time", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\nThe paper addresses a fundamental problem in diffusion-based panorama generation masked autoregressive modelling, which provides a theoretically grounded solution. The proposed innovations include dual-space circular padding and translation consistency loss which effectively tackle panoramic-specific challenges like boundary discontinuities and geometric distortions. The proposed PAR method elegantly unifies T2P, PO, and editing tasks within one autoregressive framework, reducing complexity compared to task-specific pipelines (e.g., Omni², which relies on meticulously designed joint training datasets to align heterogeneous tasks, inevitably introducing complexity and domain bias). PAR achieves SOTA FID (37.37) for text-to-panorama generation on Matterport3D dataset, outperforming larger diffusion models (PanFusion: 45.21 FID). It excels in outpaiting task (32.68 FID) and shows compelling qualitative results, avoiding artifacts/repetition in baselines. PAR laos shows strong zero-shot performance on OOD data (SUN360) and editing tasks.\n\nWeaknesses\n\nText fidelity seems to be a room for performance according to the CLIP scores comparing to PanorLiama. Omni² is only discussed conceptually but not presented in the experimental stage, however when reading I was waiting to see such an experiment. The training/inference costs are not evaluated and presented in the paper, making practical trade-offs unclear. Autoregressive advantave is overemphasized when describing \"avoiding i.i.d. assumptions\", because it is inherent to the class of autoregressive models.\n\nClarity. The text is well-structured and mostly clear. The tables and figures are clear and easy to follow.\n\nSignificance. The proposed approach addresses critical problems in panorama images generation, which are with practical and useful in VR/AR, robotics and other applications. The code/data release commitment enhances reproducibility.\n\nOriginality. The dual-space circular padding and translation consistency loss are novel and well-motivated. Several tasks unification via masked autoregressive modeling is a widely-used technique, but not for this type of data, which can also be stated as an emergency property of the unified architecture.", "was_revised": true, "final_justification": "I will remain my score. The authors answered the questions and agreed with the comments", "rebuttal": "Thank you for recognizing our strengths: 1. The proposed dual-space circular padding and translation consistency loss are novel and well-motivated. 2. The text is well-structured. The tables and figures are clear and easy to follow. 3. Strong performance. We provide more clarifications below.\n\nQ1: Failure cases analysis may improve the paper.\n\nA1: Thanks for your suggestions. Since visualization results cannot be provided in the rebuttal stage, we will add the failure cases analysis in the next draft. According to the visualizations in the main manuscript, the generated images may present unrealistic results in some parts, and there is still a gap compared with real photos, such as some details and textures. This is a common problem of generative models. We believe that scaling data can further improve the generation quality, and we leave it as future work.\n\nQ2: Comparison with Omni2.\n\nA2: As the data, code, and models of Omni2[1] are unavailable, we compare Omni2 with our PAR from two perspectives: dataset construction and modeling. For dataset construction, Omni2 requires complex data engineering, including the collection of over 60k images for various tasks, the application of GroundedSAM and LAMA, with multiple rounds of manual selection for data filtering. On the contrary, we use Matterport3D without specific data preprocessing.\n\nFrom a modeling perspective, multi-view generation (Omni2 utilizes six perspective views of a cube) presents the following challenges:\n\n1. Higher computational cost. Using six 512x512 perspective views of a cube instead of a single 512x1024 panorama results in approximately nine times the computational overhead due to the self-attention mechanism.\n\n2. Poorer global understanding. Due to the existence of multiple views, the model is prone to duplication and requires more complex mechanisms to achieve harmony across the entire scene.\n\n3. More seam issues. Multi-view approaches must handle more seams at the boundaries between views, which increases the difficulty of achieving seamless stitching and can introduce visible artifacts.\n\n\nQ3: Training and inference time.\n\nA3: Training time: It takes about 18h to train our 0.3B model, and 2 days for the 1.4B model on a single node with 8 NVIDIA A100-80G GPUs. \n\nInference tine: We benchmark the inference speed for the 0.3B model with different autoregressive steps using one NVIDIA A100 GPU with a batch size of 8. The denoising steps for MLP are 25.\n\n| AR steps | 16 | 32 | 64 |\n|---|---|---|---|\n| Inference speed (sec/image) $\\downarrow$ | 3.02 | 5.49 | 10.03 |\n\n\nQ4: CLIP score compared to PanoLlama.\n\nA4: Our model is slightly inferior to PanoLlama in terms of CLIP score, which we argue is due to the gap in panoramic geometry and CLIP's domain knowledge. In Fig.3, the generated results of PanoLlama are more similar to perspective images, while ignoring the panoramic geometry. Since CLIP is pre-trained on massive perspective images, it might have a preference for perspective views.\n\n[1] Omni2: Unifying Omnidirectional Image Generation and Editing in an Omni Model, ACM MM 2025."}, {"review_id": "JcI6n4GQFX", "reviewer": "Reviewer_YePb", "summary": "This paper introduces a novel method for generating panoramic images from various input modalities, including text and other images. The core contribution lies in adapting masked autoregressive modeling to panoramic image generation and integrating inherent panoramic properties, such as cyclic translation consistency, into the training process. A circular padding approach is also employed to prevent discontinuities. The authors validate their approach through experiments on established datasets like Matterport3D and Sun360.", "questions": "- Please address the question regarding the blurriness at the poles mentioned in the weaknesses section.\n- Could you elaborate on the advantages and disadvantages of autoregressive modeling compared to multi-view diffusion models for panorama generation?", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- Innovative Approach: The application of masked autoregressive modeling for panorama generation is a compelling idea that effectively addresses key challenges in the field.\n- Well-Structured and Comprehensive: The paper is well-written, and the experiments, including the chosen baselines and metrics, are plausible.\n- Spatial Consistency: Generated panoramas exhibit strong spatial consistency, outperforming several baselines (as shown in Fig. 3).\nEffective Translation Equivariance Evaluation: The evaluation of translation equivariance is thorough.\n- Strong Generalization: Impressive results on out-of-distribution (OOD) data highlight the model's excellent generalization capabilities.\n\nWeaknesses:\n\nImage Quality and Photorealism: \n- Despite achieving spatial consistency, the panoramic images often lack photorealistic quality. Examples include the washing/drying machines in Fig. 3 and the pool table, which contrasts with recent approaches like CubeDiff (Kalischek et al. ICLR 2025) and Generating 3D Worlds (Schwarz et al. arxiv 2025).\n- Significant blurry regions are present at the poles in almost all provided results. The origin of this blurriness needs clarification.\n\nUnclear Methodology:\n- Denoising MLP: The proposed small denoising MLP is insufficiently explained and lacks comprehensive ablation studies. Detailed information regarding its architecture, noise schedule, and number of denoising steps is required.\n- Training: It is unclear whether the encoder and decoder are trained on ERP (Equirectangular Projection) panoramas.\n- Patchification for ERP Images: The exact mechanism of patchification (line 156) for ERP images needs further explanation.", "was_revised": true, "final_justification": "After reading all discussions and reviews. I increase my initial rating from 3 to 4. The authors provided insights in the blurryness issue at the poles and a comparison to A more recent approach.", "rebuttal": "Thank you for recognizing our strengths: 1. Innovative approach. 2. The paper is well-written, and the experiments, including the chosen baselines and metrics, are plausible. 3. Strong spatial consistency. 4. Strong Generalization. We provide more clarifications below.\n\nQ1: Lack photorealistic quality compared with CubeDiff and 3D Worlds.\n\nA1: First, as the code and models of CubeDiff[1] and 3D Worlds[2] are unavailable, we carefully check the visualization results in their manuscript and project page. **Incorrect generation results also exist**. Examples include the unrealistic desk (CubeDiff project page, Living Room), twisted table (CubeDiff project page, Alice 2), peach tree on the wall (3D Worlds, Fig.1), and blurred and distorted walls (3D Worlds, Fig.1). \n\nSecond, CubeDiff and Generating 3D Worlds focus on the outpainting task, in which the ground truth is projected into the center of the panoramic image, which can easily produce a more realistic feeling to human eyes. \n\nThird, the training data volume. We use the Matterport3D dataset, which contains 9k images for training. On the contrary, CubeDiff collects around 48k images from multiple data sources, and 3D Worlds is trained on DL3DV-10K, which includes 51.2 million frames.\n\n\nQ2: Blurry regions are present at the poles.\n\nA2: Blurry regions at the poles is **an inherent property of the Matterport3D dataset**. In other words, all ground truth images in the Matterport3D dataset have blurriness near the poles. This phenomenon is also visible in the visualization results of PanFusion[3].\n\n\n\nQ3: Details and ablations of the denoising MLP.\n\nA3: We follow NOVA[4] for the construction of the denoising MLP. The MLP has multiple stacked blocks. Within each block, the input is first processed by AdaLNZero to get gating coefficients. It is then transformed using a projection layer consisting of two linear layers with silu activation. The result is then LN-normalized and multiplied by the gating coefficients, and finally combined with a residual connection. We use the Euler solver with 25 denoising steps.\n\nHere is the ablation study for different denoising steps with our 0.3B model. Few steps lead to integral approximation errors, while too many steps lead to cumulative errors.\n\n| Denoising step | 10 | 20 | 30 | 40 |\n|---|---|---|---|---|\n| FID $\\downarrow$ | 41.57 | 40.19 | 41.98 | 43.77 |\n\n\n\n\nQ4: Whether the encoder and decoder are trained on ERP panoramas.\n\nA4: We use the default VAE encoder and decoder from NOVA **without any specific fine-tuning**. During the training phase, VAE parameters are frozen, only transformer and MLP parameters are trainable.\n\n\nQ5: The mechanism of patchification in L156.\n\nA5: We employ the patchification operation similar to ViT [5]. Specifically, after obtaining the latent feature maps from the VAE encoder, we apply a 2x2 convolution with a stride of 2. This operation converts spatial feature maps into a sequence of tokens, serving as an input for the subsequent transformer encoder.\n\n\n\nQ6: Discussions on the advantages and disadvantages of autoregressive modeling compared to multi-view diffusion models for panorama generation.\n\nA6: Advantages:\n\n1. Computational Efficiency:\nMulti-view diffusion models require generating several perspective images (e.g., six views of 512x512 to cover a 512x1024 panorama), resulting in around 9 times computational cost due to self-attention.\n\n2. Global Consistency:\nCoordinating multiple views demands complex attention mechanisms to ensure coherence, and struggles to maintain a consistent global understanding — sometimes leading to duplicated or inconsistent content.\n\n3. Fewer Seam Issues:\nMulti-view approaches must handle more seams at the boundaries between views, which increases the difficulty of achieving seamless stitching and can introduce visible artifacts. AR modeling generates the panorama as a whole, inherently avoiding these seam problems.\n\n\nDisadvantages:\n\nSince our model directly generates the entire panoramic image, it needs to use panoramic data to drive the pre-trained model (trained on perspective images) to adapt to the panoramic domain knowledge, which might be less efficient than multi-view modeling because it directly handles perspective images.\n\nMoreover, research in AR-based panorama generation is still limited, and optimal solutions for this task remain to be discovered. We hope our work can inspire further progress in this direction.\n\nWe will fruther include these comments in the refined draft.\n\n[1] CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation, ICLR 2025.\n\n[2] A Recipe for Generating 3D Worlds From a Single Image, Arxiv 2025.\n\n[3] Taming Stable Diffusion for Text to 360 Panorama Image Generation, CVPR 2024.\n\n[4] Autoregressive Video Generation without Vector Quantization, ICLR 2025.\n\n[5] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, ICLR 2021."}, {"review_id": "KxXKuCQR2u", "reviewer": "Reviewer_Pih7", "summary": "This paper proposes a panoramic autoregressive model which integrates text and image-conditioned generation within a single architecture. In addition, it proposes specialized designs, including dual-space circular padding and a translation consistency loss.", "questions": "The same as weaknesses:\n1. add inference time comparison: PAR model vs. other models time comparison\n\n2.circular padding increases the image size and increases the inference cost, can you also show the time comparison in ablation study?\n\n3.Outdoor generalization result is poor, and so is there any plan about improving outdoor generation result.", "limitations": "yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1.PAR implements unified conditioning   that   separately processes text prompts and image inputs through a singular framework.\n\n2.This paper uses detailed comparative experiments and ablation studies to validate its effectiveness.\n\nWeaknesses:\n\n1.inference time comparison: PAR model vs. other models time comparison\n\n2.circular padding increases the image size and increases the inference cost, can you also show the time comparison in ablation study?\n\n3.Outdoor generalization result is not good without training on outdoor panoramic dataset.", "was_revised": true, "final_justification": "I have read all the reviews and discussions. The authors have answered all my questions. I will maintain my score (border accept 4).", "rebuttal": "Thank you for recognizing our strengths: 1. Unified framework. 2. Detailed experiments and ablation studies. We provide more clarifications below.\n\nQ1: Inference time comparison with other model.\n\nA1: We compare PanFusion[1] with our 0.3B model using one NVIDIA A100 GPU with a batch size of 8. The AR steps are 64, and the denoising steps for MLP are 25.\n\n| Model | PanFusion | Ours |\n|---|---|---|\n| Inference speed (sec/image) $\\downarrow$ | 28.91 | 10.03 |\n| FID $\\downarrow$ | 45.21 | 41.15 | \n\n\nQ2: Inference time regarding circular padding.\n\nA2: In the inference stage, the circular padding only participates in the VAE decoding phase, which transforms latents into pixels. This means that circular padding does not bring about additional computational cost in the transformer and MLP, which account for the majority of inference time.\n \n\nThe table below shows the inference speed for different padding ratios with AR step=16. \n\n| Padding Ratio | 0 (No padding) | 0.0625 | 0.125 | 0.25 | 0.5 |\n|---|---|---|---|---|---|\n| Inference speed (sec/image) $\\downarrow$ | 2.99 | 3.01 | 3.02 | 3.04 | 3.05 |\n| Additional costs | 0 | 0.67% | 1.00% | 1.67% | 2.01% |\n\nPlease note that: \n\n1. The proportion of time occupied by circular padding can further decrease as AR steps increase.\n\n2. From Fig.8 in the main manuscript, the synthesized results with a padding ratio of 0.0625 are close to convergence.\n\n\n\nQ3: Is there any plan to improve the outdoor generation result?\n\nA3: Thanks for your suggestion. Yes, we plan to curate and annotate a large-scale, high-resolution outdoor panoramic dataset with diverse scenes and lighting conditions. This dataset will help reduce the domain gap and provide richer context for model training. Additionally, we are considering incorporating domain-specific data augmentation and leveraging pretraining on related outdoor datasets to enhance generation quality further. We believe these steps will benefit outdoor scene synthesis in future work.\n\n\n[1] Taming Stable Diffusion for Text to 360 Panorama Image Generation, CVPR 2024."}, {"review_id": "0Be3GvkmtW", "reviewer": "Reviewer_1sbD", "summary": "This paper introduces the Panoramic AutoRegressive (PAR) model, a panoramic image generation framework based on masked autoregressive modeling. Previous methods based on diffusion models had critical limitations that a single model couldn’t handle both panorama generation and outpainting tasks, and that they faced the constraints of iid assumptions which is inherently violated by the equirectangular projection during panorama generation. To resolve these issues, the authors utilize a conditional masked autoregressive modeling framework that removes the iid assumptions and also handles generation and outpainting tasks in an unified manner. Moreover, PAR suggests novel techniques tailored for panorama generation, including dual-space circular padding and a translation consistency loss. Consequently, PAR achieves competitive results compared to previous state-of-the-art diffusion-based methods and shows strong scalability and zero-shot generalization capabilities.", "questions": "* Although the paper is well-structured, the overuse of abbreviations seems to harm its readability. For example, using \"T2P\" for \"text-to-panorama\", \"CS\" for \"CLIP score\", \"DS\" for \"discontinuity score\" were often confusing while reading the paper and had to revisit the original definition repeatedly.\n\n* Figure 2 doesn't seem very informative for understanding the architecture of PAR. Providing a more detailed visual overview of the proposed framework would help the authors understand the main idea. For instance, it would be better to clarify how the input and output of the \"Transformer f\" are structured, instead of solely representing it using a letter.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strength**\n\n* The paper is clearly written and easy to follow.\n\n* Adopting masked autoregressive modeling for panoramic generation is a reasonable design choice given the limitations of previous diffusion-based approaches.\n\n* PAR outperforms the main baselines in both panorama generation and outpainting tasks, and the qualitative comparisons further show its advantage over previous methods.\n\n* Thorough ablation studies for the proposed techniques - circular padding and consistency loss - make it clear that the design choices in PAR lead to the intended outputs.\n\n**Weakness**\n\n* The evaluation setup for panorama outpainting is confusing. Why were AOG-Net and 2S-ODIS used as baselines for the quantitative comparion, yet PanoDiff and Diffusion360 were compared on the OOD dataset? Was it unavailable to conduct quantitative comparisons with PanoDiff and Diffusion360 on the Matterport3D dataset?\n\n* I couldn't find any visualizations of **perspective projections** of the generated panoramas. I believe some visualizations along with qualitative comparisons on the perspective projections are necessary to versity whether the generated outputs from PAR respect the geometric properties of equirectangular panoramas.\n\n* Some previous works on panoramic generation are not compared as baseline, while they are included in the related work. Both DiffPano [1] and CubeDiff [2] were mentioned in the related work section for **Conditional Panoramic Image Generation**. I am curious if the authors could not compare with these methods due to the unavailability of their code and models.\n\n* The idea of adding paddings to the left and right sides to ensure continuity for panoramas were previous introduced in StitchDiffusion [3]. Can you please explain whether the methods have significant differences, or they are similar techniques?\n\nOverall, PAR seems to produce high-quality panoramas by adopting masked generation framework with several techniques to ensure panoramic properties. However, there are multiple uncertain points regarding its novelty and evaluation settings, as mentioned above. I believe these critical points should be addressed to clearly understand the advantage of PAR over existing works.\n\n[1] DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion, NeurIPS 2024\n\n[2] Cubediff: Repurposing diffusion-based image models for panorama generation, ICLR 2025\n\n[3] Customizing 360-Degree Panoramas through Text-to-Image Diffusion Models, WACV 2024", "was_revised": true, "final_justification": "**(After Initial Rebuttal)**\n\nWhile the authors' rebuttal has provided clarifications on most of the raised concerns, multiple concerns regarding the methods novelty and the comparison with previous methods yet remain unresolved.\n\n* **[Q1]** Missing the citation of StitchDiffusion [1] and not discussing the difference/similarity with their padding technique in the original manuscript remains a critical limitation and raises questions about the novelty and completeness of the proposed method. Moreover, the third point made by the authors is not convincing, since the naive comparison with different settings in different works cannot convey a fair comparison. Without a systematic evalution or ablation, it is difficult to support the authors' claims.\n\n* **[Q2]** The additional experiments provided are not helpful in understanding the advantage of PAR, because (1) the evaluation only contains PanoDiff while the authors discussed both PanoDiff and Diffusion360, and (2) the evaluation metrics used in the manuscript are missing, and only FID is used.\n\n**(After Additional Discussions)**\n\nThe additional clarifications and experiment results resolved my remaining concerns above. Therefore I raise the rating to **borderline accept (4)**.\n\n---\n\n[1] Customizing 360-Degree Panoramas through Text-to-Image Diffusion Models, WACV 2024.", "rebuttal": "Thank you for recognizing our strengths: 1. Clearly written paper. 2. Reasonable design choice. 3. Strong performance. 4. Thorough ablation studies. We provide more clarifications below.\n\n\nQ1: The difference with StitchDiffusion regarding the proposed dual-space circular padding strategy.\n\nA1: At a high level, both methods aim to mitigate the inconsistency between the left and right ends of the panorama. However, our proposed dual-space circular padding is **fundamentally different** from StitchDiffusion[1]. \n\n1. From methodology, StitchDiffusion focuses on the denoising model, but our method focuses on VAE. StitchDiffusion pads the latent and then denoises twice on the stitch area, which increases the huge computation cost. On the contrary, the extra cost brought by our method is negligible (please refer to our answer for Reviewer Pih7's Q2).\n\n\n2. From motivation, StitchDiffusion forces the model to fuse seam areas during inference. On the contrary, our pre-padding makes the model learn semantic consistency during the training phase, and our post-padding achieves pixel-level consistency in the inference stage.\n\n\n3. From the results, we argue that simply focusing on pixel consistency cannot solve the problem of semantic inconsistency, which is demonstrated in Fig.8 of our main manuscript. Moreover, Semantic inconsistency (houses of different heights and colors) is observed in Fig.7c of StitchDiffusion's manuscript.\n\n\n\nQ2: The reason for choosing the outpainting baselines.\n\nA2: We select two **peer-reviewed works with publicly available code**, AOG-Net[2] and 2S-ODIS[3], as the baselines for the Matterport3D dataset. We choose PanoDiff[4] and Diffusion360[5] as OOD baselines because the model weights trained on the SUN360 dataset are provided in the official repo. Following the reviewer's suggestion, we further reproduced PanoDiff and compared it with our 1.4B model on panorama outpainting task. The training dataset is Matterport3D and the text prompt is set to empty. Our model has advantages in terms of FID metric.\n\n|  |PanoDiff | Ours |\n|---|---|---|\n| FID $\\downarrow$ | 65.11 | 41.63 |\n\n\n\nQ3: Comparisons with the methods whose codes are unavailable.\n\nA3: We summarize the code and model availability for the mentioned methods in the table below:\n\n| Model | Training Code | Inference Code | Model Weights |\n|---|---|---|---|\n| Diffusion360 | NA | Available | Available |\n| DiffPano | NA | NA | NA |\n| CubeDiff | NA | NA | NA |\n\nAs shown, both DiffPano[6] and CubeDiff[7] do not provide publicly available code or pretrained model weights, which makes direct quantitative comparison challenging. Despite this limitation, we have carefully reproduced the DiffPano pipeline according to their paper and compared with our 1.4B model on text-to-panorama task. The training dataset is Matterport3D. The results are as follows, and our model has advantages in terms of FID metric.\n\n| Model | DiffPano | Ours |\n|---|---|---|\n| FID $\\downarrow$ | 53.29 | 37.37 |\n\n\n\nQ4: Visualizations of perspective projections.\n\nA4: In Tab.2, our method outperforms baseline methods on the FID-h metric. For FID-h, we horizontally sample 8 perspective images with 90-degree FoV for each panorama and then calculate their FID. As additional visualization results are not permitted in the rebuttal stage, we will add them in the next draft. \n\n\n\nQ5: The overuse of abbreviations seems to harm its readability.\n\nA5: We apologize for the inconvenience. The abbreviations for tasks and metrics are introduced in L78-79 and L207-213, respectively. We will try to make the explanations more explicit in the next draft, including using full names where possible, or providing a brief introduction to the abbreviations in the captions.\n\n\n\nQ6: Visual overview of the proposed framework. How the input and output of the \"Transformer f\" are structured.\n\nA6: Thanks for your suggestion. Our \"Transformer f\" follows NOVA[8] and is described in Sec.3.3. Briefly, it takes patchified visual tokens (from the VAE encoder), masking indicators, and text embeddings as inputs. It outputs a conditional signal to drive the subsequent denoising network MLP. We will add a visual overview of the framework in the next draft.\n\n\n[1] Customizing 360-Degree Panoramas through Text-to-Image Diffusion Models, WACV 2024.\n\n[2] Autoregressive Omni-Aware Outpainting for Open-Vocabulary 360-Degree Image Generation, AAAI 2024.\n\n[3] 2S-ODIS: Two-Stage Omni-Directional Image Synthesis by Geometric Distortion Correction, ECCV 2024.\n\n[4] 360-Degree Panorama Generation from Few Unregistered NFoV Images, ACM MM 2023.\n\n[5] Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion Models, Arxiv 2023.\n\n[6] DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion, NeurIPS 2024.\n\n[7] Cubediff: Repurposing diffusion-based image models for panorama generation, ICLR 2025.\n\n[8] Autoregressive Video Generation without Vector Quantization, ICLR 2025."}]

Author rebuttal:

Meta-review: This paper adapts masked autoregressive modelling to panoramic images, enabling panorama generation based on different conditions, including in particular text prompts (text-to-panorama) and perspective images (outpainting). Initially the reviews were mixed, criticisms included limited visual quality of the results, including blur near the poles, as well as the absence of certain important baselines. During the discussion the authors made a big effort and were able to address the criticisms, such that the four reviewers converged to a unanimous "borderline accept" rating. The AC agrees that, despite the unresolved limitations, the novelty of adapting masked autoregressive modelling to the task, combined with the breadth of experimental results, warrant acceptance.

[side comment from the AC: it is not good practice to submit a premature paper and only fix the loose ends for the rebuttal. E.g., if you are aware that the poles are blurry because of artefacts in Matterport3D, you should *first* run experiments on another dataset without that problem to verify that it is a dataset issue, *then* submit the paper. Similarly, "their quality is better because they train on more data" is not a very good justification - as long as the additional data is available openly at no cost, it constitutes the current state-of-the-art testbed for research and use should also use it (possibly with an ablation study about dataset size)]
Decision: Accept (poster)