Arbitrary-Shaped Image Generation via Spherical Neural Field Diffusion
Authors: Jiyuan Xia
Yuanshen Guan
Ruikang Xu
Zhiwei Xiong
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Models
Image Generation
Spherical Neural Field
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: ASIG learns a spherical latent with diffusion and uses a spherical neural field to control FOV, viewpoint, and resolution, enabling high-quality perspective/panorama/fisheye/irregular synthesis from a single model.

Abstract: Existing diffusion models excel at generating diverse content, but remain confined to fixed image shapes and lack the ability to flexibly control spatial attributes such as viewpoint, field-of-view (FOV), and resolution. 
To fill this gap, we propose Arbitrary-Shaped Image Generation (ASIG), the first generative framework that enables precise spatial attribute control while supporting high-quality synthesis across diverse image shapes (e.g., perspective, panoramic, and fisheye).
ASIG introduces two key innovations: (1) a mesh-based spherical latent diffusion to generate a complete scene representation, with seam enforcement denoising strategy to maintain semantic and spatial consistency across viewpoints; and  (2) a spherical neural field to sample arbitrary regions from the scene representation with coordinate conditions, enabling distortion-free generation at flexible resolutions. 
To this end, ASIG enables precise control over spatial attributes within a unified framework, enabling high-quality generation across diverse image shapes. Experiments demonstrate clear improvements over prior methods specifically designed for individual shapes. Code is available at https://github.com/xjyjjy/ASIG.

Published as a conference paper at ICLR 2026
ARBITRARY-SHAPED IMAGE GENERATION VIA SPHERICAL NEURAL FIELD DIFFUSION
Jiyuan Xia, Yuanshen Guan, Ruikang Xu, Zhiwei Xiong University of Science and Technology of China, Hefei, China (jyxia, ysguan, xurk)@ma .ustc. edu. en, wwx.ongeustc..ed..
ABSTRACT
Existing diffusion models excel at generating diverse content, but remain confined to fixed image shapes and lack the ability to flexibly control spatial attributes such viewpoint, field-of-view (FOV), and resolution. To fill this gap, we propose Arbitrary-Shaper Image Generation (ASIG), the first generative framework that enables precise spatial attribute control while supporting high-quality synthesis across diverse image shapes (e.g.. perspective, panoramic, and fisheye). ASIG in- troduces two key innovations: (1) mesh-based spherical latent diffusion to gen- erate a complete scene representation, with seam enforcement denoising strategy maintain semantic and spatial consistency across viewpoints; and (2) spher- ical neural field to sample arbitrary regions from the scene representation with coordinate conditions, enabling distortion-free -free generation at flexible resolutions. To this end, ASIG enables precise control over spatial attributes within a unified framework, enabling high-quality generation across diverse image shapes. Exper- iments demonstrate clear improvements over prior methods specifically designed for individual shapes. Code available at http:///ihhhubooo//yii///A/
Text Condition A photo of house by manquil lake. calm water reflections. bright daylight
Courdinate Condition (t, (..
000.)) IOOD
01
Spherical
Arbimey (Arb.)
Neural Field
Staped Imsge
Subdivsded Icoashofron Unfakling
Perspeetive Image
Pommromic Image
Fishew Image
Ireggulrr Imagg
Arb
Virapoint
Vleupaint
FOV
Reselution
Aspeet Rotis
Figure ASIG Overview. Our framework generates complete spherical scene representation via a mesh-based spherical latent diffusion and enables distortion- free sampling through spherical neural field. The proposed framework provides flexible control over FOV, viewpoint, and resolution, supporting high-quality generation across diverse image shapes, such perspective, panoramie, and fisheye views. Generation at arbitrary aspect ratios naturally emerges from the joint control of FOV and resolution. As extended cases, irregular images also be generated by jointly controlling FOV and resolution.
INTRODUCTION
Diffusion-barse generative models have recently achieved remarkable success, powering wide range of applications artistic creation, design, and beyond (Ruiz et al., 2023; Brooks et al., 2023;
"orrrsponniigg Author

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Published as a conference paper at ICLR 2026
Blatmmann et al., 2023; Guan al., 2025; Gao et al., 2024). However, critical challenge lies in en- abling precise and explicit control over the spatial attributes of the generated content. While Stable Diffusion (SD) (Rombach et al., 2022) allows conditini image generation and SDXL (Podell et al., 2023) enables high-resolution generation, perspective image generation methods (Dhariwal & Nichol, 2021; Saharia et al., 2022; Peebles & Xie, 2023; Ramesh et al., 2022) still lack explicit control over viewpoint, field-of-view (FOV), and resolution. Recent arbirrary-resolutonn exten- sions (Chen al., 2024; Kim & Kim, 2024) enable flexible output resolutions but retain unchanged FOV and viewpoint, producing merely upsampled detail rather than new scene content, which limits their practical effectiveness.
In parallel, panoramic generation methods (Tang et al., 2023; Zhang et al., 2024; Sun et al., 2025; Ni et al., 2025) can produce full-view imagery, but they remain tied rixeltresouution panoramas at single central viewpoint. New perspectives must be obtained through projection, which inevitably introduces distortion and fidelity loss. Thus, existing paradigms remain specialized to either per- spective or panoramic generation and lack unified framework that jointly controls viewpoint, FOV, and resolution while ensuring high-quality generation across different image shapes.
To fill this gap, we present ASIG, the first framework that unifies controllable generation of view- points, FOVs, and resolutions, while supporting diverse image shapes (e.g., perspective, panoramic, and fisheye). ASIG introduces two key innovations: (1) a mesh-based spherical latent diffusion to generate a complete scene representation built on a multi-level subdivided icosahedron, with seam enforcement denoising strategy to maintain semantic and spatial consistency across viewpoints; and (2) spherical neural field that incorporates feature extraction modules aligned with spherical topol- ogy, allowing arbitrary regions of the scene representation to be sampled under coordinate condi- tions, thereby enabling distortion-free generation at flexible resolutions.
To this end, ASIG establishs unified framework for flexible. high-fidelity, and geometry- aware image generation. Beyond extending perspective generation with arbitrary FOVs and resolutions, ASIG naturally generalizes to panoramic and fisheye images, surpassing the generation quality of prior specialized generative methods.
Our main contributions are summarized as follows:
We propose ASIG, the first unified diffusion-based framework that enables explicit control over viewpoint, FOV, and resolution, supporting diverse image shapes generation.
We propose mesh-based spherical latent diffusion that generates complete scene spherical representation with seam enforcement denoising to maintain semantic and spatial consistency across viewpoints.
We introduce a spherical neural field that enables coordinate-condiiioned distortion- sampling for arbitrary regions at flexible resolutions.
Extensive experiments show that ASIG enables precise control over spatial attributes, generalizes across diverse image shapes, and surpasses specialized methods significantly.
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RELATED WORK
Perspective Image Generation Perspective image generation with diffusion models has been tensively explored. Dhariwal & Nichol (2021) first demonstrated that diffusion models could surpass GANs in producing high-fidelity images, establishing diffusion synthesis. Stable Diffusion (SD) and SDXL (Rombach et al., 2022; Podell et al., 2023) further ad- vanced this direction by operating in latent space, incorporating text conditioning via cross-attention, and scaling effectively to high resolutions. However, they provide only weak spatial control. Image- GEN (Brooks et al., 2023) extends diffusion with instruction-driven editing capabilities, enabling controllable modifications but remaining confined to fixed image grids. Diffusion Transformers (DiT) (Peebles & Xie, 2023) replace convolutional UNets with pure Transformer backbones. achiev- ing strong scalability and quality in perspective synthesis, yet they are still tied to fxed-resolutie training and lack explicit viewpoint FOV control. Overall, these methods implicitly capture cor- relations among resolution, content, FOV, and viewpoint from training data, without any explicit design for spatial attribute control, which fundamentally limits their ability to offer precise control.
powerful paradigm for image
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Subdivision Level
1 Subdivided Icosahedron Unfolding
2 Regular Gridding
Illustration of L-subdivided icosahedron. Starting from a regular icosahedron (12 vertices, 20 triangular faces, 10 diamond-shaped faces), each subdivision quadrisects every face and repro- jects the new vertices onto the sphere. This structure provides a distortion-free representation of the sphere, while the hierarchical subdivision naturally aligns with the multi-scale architecture of diffusion UNet, making it well-suited for generative modeling.
Figure 2:
Panoramic Image Generation Recent panoramic image generation methods mostly learning in the equirectangular projection (ERP) domain, but the inherent non-uniform distortions in ERP hin- der accurate generative modeling. Stitching-basee based methods (Tang et al., 2023; Tuli al., 2025) assemble perspective views into panoramas but suffer from limited FOV and cross-view inconsisten- cies. ERP-based diffusion methods (Zhang al., 2024; Ni al., 2025) directly train on panoramas, while cube- or sphere-au sphere-aware aware variants (Kalischek et al., 2025; Sun et al., 2025) attempt to improve continuity via overlapping faces or spherical convolutions. Despite these advances, these methods remain tied to fixed rectangular grids, producing panoramas of fixed resolution from single central viewpoint. Novel perspectives require projection, causing distortion and blur, while the non-uniform sampling of ERP further undermines semantic and spatial consistency.
Neural Fields for Image Generation Neural fields represent signals with coordinate- net- works and have shown strong results in low-level vision tasks (Chen et al., 2021; Xu et al., 2025; Yao et al., 2024) and image generation. Prior methods (Chen al., 2021; Lee et al., 2022; Yoon et al., 2022) adapt neural fields for arbitrary-scale super-resolution or spherical image representa- tion. In image generation, INFD (Chen et al., 2024) and Kim & Kim (2024) further propose to generate perspective images at arbitrary scales, but remain limited to fixed aspect ratios, viewpoints, and FOVs. In contrast, we couple spherical neural field with mesh-based spherical latent diffu- sion, extending neural fields beyond arbitrary resolution to support arbitrary viewpoints and FOVs, while generalizing image generation from perspective to panoramic, fisheye images.
3 METHOD
Overview ASIG integrates mesh-based spherical latent diffusion with spherical neural field to achieve precise control over spatial attributes. During spherical latent diffusion, the UNet takes a text prompt together with initial Gaussian noise tensor and progressively denoises it into the spherical latent, represented in the patch-based format shown in Fig. 2. As shown Fig. 3, Scam-Aware Padding (SAP) enforces semantic consistency across neighboring patches, ensuring complete spa- tial representation. SAP is likewise applied the pherical neural field, where spherical residual blocks extract multi-scale features from the VAE decoder. Given coordinate conditions based resolution, viewpoint, and FOV, a convolutional latent sampler maps the features to RGB values at the specified spatial attributes.
3.1 MESHI-BASED SPHERICAL LATENT DIFFUSION
Mesh-based Spherical Latent We introduce a mesh-basee spherical latent representation built on L-subdivided icosahedron. This representation aligns with the multi-scale hierarchy of the UNet diffusion and the VAE model. As illustrated in Fig. 2, at subdivision level L = O, each pair of adjacent triangular faces is merged into diamond-shaped patch, resulting in 10 non-overlapping
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Tar Conditiin
Coordiantes Conditio (u.,;8.0.PO0)
SR- BR-Blocks
Arbitrary
Sampler
1)
Resolution FOV
Spherical Neural Field
Scam Enforcement
Spherical
Arbilrary Viewpoint
Seam--wware Padding
Lunky Jebs
Sphetical
Panoramic Image
Spherical Residual Blocks
Seam- Ssam-Awree Padding Conv
Cross-AAsatiin Blocks
Fisheye Image
Latent Sampler
Element Adding
Receptive Field Remag
Illustraioon of the ASIG generation pipeline. Starting from Gaussian noise, mesh- based spherical denoising UNet with Seam-Aware Padding performs T denoising steps to produce complete scene spherical latent aligned with the text condition. The latent is decoded by VAE decoder to produce multi-scale features, which are processed by spherical residual (SR) blocks for hierarchical refinement. Finally, a coordinate-conditinss convolutional latent sampler generates high-quality images of diverse shapes.
Figure 3:
patches that cover the entire sphere. For general subdivision level L, each original face is further divided into 4 triangular faces. Within each patch, we unfold the diamond into a rectangular grid and map every triangular face to a unique pixel by sampling its barycenter, thus establishing a one- to-one correspondence:
FL + Mp E RHuxWaxd
p= 10,
(1)
where FL is the set of triangular faces in patch p, and Mp denotes its rectangular feature map. The spatial resolution of each patch grows with the subdivision level L:
(HL. WL) =
(2)
so that L, = 5 yields 64 x 64 feature map and L = 6 yields 128 x 128 feature maps. This con- struction preserves spherical adjacency, maintaining feature integrity while enabling standard 2D convolutions on unfolded patches. More importantly. it naturally aligns with the multi scale UNet of diffusion and VAE models, with subdivision levels corresponding to different feature resolutions.
Seam-Aware Padding However, each patch corresponds to diamond-shaped region on the sphere. When unfolded into rectangular grid, only the diamond area valid while the remain- ing cells are zero-initialized. This leads to two issues: (i) convolutional kernels cannot access in- formation across patch boundaries during inference, causing contextual discontinuities; and (ii) the zero-padded regions introduce artificial seams between adjacent patches.
(3) with indices taken modulo 10 to respect the cyclic ordering of patches. As shown in Fig. 3, explicitly annotate one example set of patch indices to illustrate how neighbors are arranged on the unfolded layout, and how they are used for Seam-Aware Padding. The padded feature map Mp. (u, isis then defined as
(pp)) = (Pi-2, Pi-1: Pi+1: Pi+2], }
[Mp. (u, v),
(u,, v) valid region of pc,
(p
(UI.-,,,Mp (u', o')), (u, v) E padded region, Pj (P:)))
where Ipp.pp, denotes the geometric remapping along the mesh adjacency between p; and its neigh- bor Pj. practice, we exploit the connectivity graph of the subdivided icosahedron to determine adjacency relations and boundary correspondences.
By applying Seam-Aware Padding to all convolutional layers both the diffusion UNet and the VAE decoder, convolutional kernels perceive continuous features across patch boundaries while respecting spherical geometry. This strategy effectively removes artificial seams, ensures consistent semantics across the entire sphere, and allows the receptive field to extend smoothly over the mesh.
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Published as a conference paper at ICLR 2026
Seam Enforcement Denoising To address discontinuities from independently processing un- folded spherical patches, we apply SAP throughout denoising. At each timestep, the latent Ze ~ (0, is augmented by copying boundary features from mesh-adjacent neighbors x, SeamPad(z;; I.), allowing convolutions to perceive continuous spherical context. The padded la- tent is passed to the UNet with timestep Ł to predict noise, and the scheduler updates 2;-1 Step(z;,,r f,,,),)). This process repeats until t = O, yielding the seam-consistent spherical latent Zo- Notably, SeamPad(;;; L) modifies only boundary cells while keeping interiors intact, ensuring both efficiency and semantic continuity.
3.2 SPHERICAL NEURAL FIELD
While the Mesh-based Spherical Latent Diffusion yields a consistent spherical latent Zo. direct de- coding causes distortions and lacks explicit spatial control. To address this, we propose the Spherical Neural Field (SNF), which samples specified regions from Zy under coordinate conditions. SNF re- spects spherical topology and consists of two parts: spherical residual blocks, which remap receptive fields to the mesh-based structure for multi-scale feature refinement, and convolutional latent sam- pler for coordinate-basee decoding.
Spherical Residual Blocks Given the spherical latent Zg. we extract multi-scale decoder features (Z)). Each feature map is refined by Spherical Residual Blocks, where receptive fields are remapped to the icosahedron topology reduce spherical distortions. D(E) (Zo) refined with spheri- cal residual blocks (SphRes() )), then upsampled to a common resolution (U psamplel()) (.)) and finally merged along the channel dimension (Concat(-) (-)) to form the multi-scale representation. Formally,
F = Concatre (6.7.8,9) U (SphampleSShReSSe (9) (D (26)))
(5)
Convolutional Latent Sampler After obtaining the multi-scale spherical features F and the VAE-decoded RGB image D(2)), we concatenate them along the channel dimension and apply coordinate-basse based sampling followed by lightweight convolutional sampler produce the final RGB output. Given a target viewpoint (0, 6) and FOV, we define a sampling region D000, ø, FOV) on the sphere. The projection function (u, V; 0, Φ, FOV) maps each output pixel (u, v) to its cor- responding coordinate on the spherical latent, based on the chosen projection type and the sampling region Q. Using these coordinates, the rendered image given by:
(6) Here, Sample(-) performs interpolation on the concatenated feature map at the projected coordi- nates, and fo is a lightweight network that maps the sampled features to RGB values. This for- mulation enables flexible image generation under arbitrary resolution, viewpoint, FOV, and image types.
I(u, v) fe Sample([F, D(Z))). (u, u; 0 6, FOV))).
(u,v) E G.
3.3 TRAINING STRATEGY
Stage 1: Spherical Neural Field Construction We freeze the VAE encoder and jointly train the spherical neural field in an end-to-end manner. Following Sec. 3.1, panoramas are converted into mesh-based patch, and ground-truth RGB images at arbitrary resolutions are sampled for supervi- sion. The model is optimized with weighted combination of pixel-wise loss L1, perceptual loss frews. and adversarial loss LUAN, balanced by coefficients λ1, Ap. and Ag. respectively:
= \1 L1 + >p LiPIS Ag LGAN-
Mesh-based Spherical Latent Diffusion Finetuning In the second stage, train the UNet from Sec. 3.1 to model the reverse diffusion process, with the VAE encoder kept frozen. Each panorama is encoded into the mesh-hased latent, perturbed with Gaussian noise, and the UNet is trained to predict the noise. To enforce cross cross-pates patch consistency, both the noisy latent z, and ground-truth noise e are augmented by the Seam-Aware Padding operator DeamPad(-,4 L), yielding the training objective:
Stage 2:
Lai Ex,.. |/eeamPa(ss L) FFeaaooSSaaaaaeeeee L), 2),,,)) 2)
where Fg is the UNet denoiser and y the text condition. This seam enforcement aligns training with inference and prevents boundary artifacts, ensuring semantic continuity across the sphere.
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Table Quantitative comparison with state-of-the- methods on perspective, panoramic, and fish- eye images. Bold and underline indicate the best and second-best results, respectively. KID* values are reported in units 10
Perspective NIQEL
Pamoruma
Fisheye
Metrie
FID
KID"L
Clip-FID
PIQEL
MUSIQT
FAED
KID"
Clip-FID
FID
KID
Clip-FIID
INFD
43.24
1.65
12.52
8.95
86.38
31.64
61.87
86.73
7.63
6.94
15.39
46.39
3.27
10.33
Kim et al.
40.77
1.87
11.86
8.46
88.61
30.98
64.98
83,88
9.87
7.77
17.96
47.86
3.47
10.56
SD Pano.
36,96
1.56
9.59
6,53
67.82
46.92
48.28
111.51
51.06
9.50
20.42
48.01
3.54
10.45
SDXL Puno.
35,97
1.55
11,00
65.39
53.16
41.36
82.13
7.53
4,90
16.50
45.13
3.03
10.04
MVDiffusion
23.17
0.93
5.84
09.29
47.53
44.33
82.24
54.74
6.71
9.91
2.40
5.26
PanFusion
21.33
86.73
32.86
37.09
1.63
17.16
2.78
LayerPano3D
53.22
3.09
13:16
82.12
39.81
54.44
66.16
5.02
3.86
12:47
48.34
3.12
10.79
PAR
37.16
1.92
6.14
9.38
89.91
28.45
65.52
35,50
1.53
1.29
6.30
28.56
2.04
4.14
SMGD
23.11
1.06
6.02
8,53
87,31
31.81
61.15
28.92
1,58
1.02
3.10
17.81
1.00
2,76
Ours
14.68
0.59
3.58
4.18
45.06
67.03
27.16
25.49
1.47
0.97
2.94
10.29
0.51
1.99
EXPERIMENT
4.1
TRAINING AND INFERENCE SETUP
The UNet and VAE weights are initialized from SDXL (Podell et al., 2023). Training proceeds in two stages. First, we freeze the VAE encoder and train the spherical neural field end-to-end for 100k iterations with AdamW (Loshchilov & Hutter, 2017) (batch size 4, learning rate 1.8x10-9), Second, we train the UNet (Ronneberger et al., 2015) for another 100k iterations with mixed precision on 8x A 100 GPUs (batch size 80), using cosine annealing learning rate schedule between 3 10-5 and x 10-7. Classifier-free guidance (Ho & Salimans, 2022) with 10% text dropout is applied. At inference, we use e-prediction with DDIM (Song al., 2020) sampling for 50 steps.
4.2 DATASETS
Following previous methods (Tang et al., 2023; Zhang et al., 2024; Sun et al., 2025), we use the Matterport3D dataset (Chang et al., 2017), which contains 10,800 panoramas. Among them, 2,000 are reserved for testing, and the remaining are used for training. All text prompt are generated using BLIP-2 (Li et al., 2023a). During training, each panorama is preprocessed into mesh patches and converted into 10 patches for training.
4.3 EVALUATION
Metrics We evaluate our method across multiple image shapes. For perspective images, we report FID (Heusel et al., 2017), CLIP--ID (Kynkäänniemi al., 2022), KID (Binkowski et al., 2018), 8), and NR-IQA metrics (NIQE (Mittal et al., 2012), PIQE (Venkatanath et al., 2015), MUSIQ (Ke et al., 2021), BRISQUE (Mittal et al., 201 For panoramas and fisheye images, we report FID, CLIP- FID, and KID, with FAED (Oh et al., 2022). Together, these metrics evaluate realism, perceptual quality, and geometric consistency across image shapes.
Baseline Methods We benchmark ASIG against a diverse set of state-of-the-arr image genera- tion approaches. The comparison includes panorama generation models MVDiffusion (Tang al., 2023), PanFusion (Zhang et al., 2024), PAR (Wang et al., 2025), LayerPano3D (Yang et al., 2025) and SMGD (Sun et al., 2025), well arbitrary-scale generation methods including INFD (Chen et al., 2024) and Kim & Kim (2024). All models are trained on the same dataset to ensure faimess. In addition, we include two baselines, IDXX-ppanoamm (Podell et al., 2023) and SD-panorama (Rom- bach et al., 2022), obtained by finetuning Stable Diffusion models with LoRA (Hu et al., 2022) on panoramic images.
Quantitative Comparison As reported in Tab. 1, ASIG consistently achieves the best perfor- mance across all metrics and image shapes. In particular, it yields substantial gains in FID, KID, and CLIP-FID for both perspective and fisheye generation, while also surpassing prior methods on panoramas. These results demonstrate that ASIG's unified design, combining mesh-based spherical latents with a spherical neural field, not only ensures semantic continuity across views but also en- ables precise control viewpoint, FOV, and resolution. Overall, ASIG establishes versatile and effective framework that outperforms all baselines.

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Perspective Image
Fisheye Image
3)
Image
1024
312
1024
Test Prompt: photo of a living room with couch and a television.
512
Arbitrary Viewpoint
Original POV
Arbitary Resolution
Arbitrary Asgeet Ratio
Arbirary FOV
In-domate indoor prompt
Viewpointi
Original Viewpoints
Arbitrey Resolation
Arbitrary Resolution
Arbitrary Vienpoint
Arbitrary Viewpoint
Text Prompt: A photo of lake with house by share, surrondeed under clouds.
Arbitrary Viewpoint
Original FOV
Arhitary Resolution
Arbitrary Aspect Ratio
Arbitrary
Out-if-alomol Guldow prompt
Original Viewpointi
Original Viewpointi
Arhitary Resolution
Arbitrary Resolution
Arbitmry Vieapoint
Arbitmrr Viewpoint
Figure 4: Qualitative results of ASIG on both in-domain (indoor) and out-of-domain (outdoor) scenes. ASIG produces high-quality perspective images with arbitrary viewpoint, FOV, resolu- tion, and aspect ratio, as well as connetry-consiseett fisheye and panoramic generations. It further demonstrates strong generalization outdoor scenes beyond the training domain.
present qualitative results of ASIG across both in-domain and out-of-domain scenarios. For perspective images, ASIG supports arbitrary viewpoints, resolu- tions, aspect ratios, and FOVs, generating results that are not only geometrically consistent but also rich in fine details, free from the blurring distortions. For fisheye and panoramic images, ASIG likewise enables arbitrary viewpoints and resolutions, while preserving seamless continuity across patch boundaries and delivering coherent global layouts. Benefiting from the mesh-based spherical latent, all projections are sampled from unified representation, ensuring high fidelity, semantic consistency, and strong cross-view coherence across diverse image shapes.
Qualitative Comparison In Fig.
Fig. 5, we present visual comparisons with baselines on both perspective and panoramic image generation, including enlarged regions for closer inspection. ASIG consistently produces sharper and more realistic textures across all settings, highlighting its superiority over competing methods. Additional qualitative results, including extensive out-of-domain cases and further visual compar- isons with baselines, are provided the Appendix to more comprehensively demonstrate the ro- bustness and generalization ability of approach.

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Perspective View
Zoomed
Perspective View 2
Zoomed
Panoramic View
Zoomed
SDX
Pank MVDidusson
SMCO
Perspective View
Zoomed
Perspcetive View 2
Zoomed
Panoramic View
Zoomed
SDAS
MVDiftusin
Punk miion
SMOD
Figure 5: Qualitative comparison with baseline methods perspective and panoramic image gener- ation. The zoomed-in regions highlight that ASIG consistently produces sharper textures and more realistic details, while maintaining global consistency across viewpoints.
ABLATION
Ablation on spherical neural field To further validate the effectiveness of our spherical neu- ral field, we enhance the baseline full-view generation model with LTEW (Lee al., 2022). LTEW
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W/O SAP
W/ SAP
Visual ablation of Seam-Aware Padding (SAP) for panorama generation. Without SAP, seams and misalignment are clearly visible, whereas SAP effectively removes artifacts and ensures spatial consistency.
Figure 6:
Table 2: Comparison on panorama quality across resolutions (pFIDL/ / KID* + denotes LTEW-
enhanced variants (Lee et al., 2022). KID values reported in units of 10
Resolution
INFD
KIM
SDXL*
MVDiffusion
PANfusion*
SMGD*
Ours
1536
36,61 /133
37.87 1,27
49.26 / 1.64
48,30/ 1.79
25.46/ 1.04
27.31/100
16.31 0.74
1024
1.49
41.86/1.5
52.79 /1.73
57.99/ 2.03
27.66 / 1.07
2.146/1133
18.76 0.86
56.36 1.78
5
1.88
59.84 1.97
64.26/ 2.76
29.24/ 1.15
31.32/1224
20.49 0.93
Table 3: Ablation on spherical residual blocks. KID* values are reported in units of 10-2.
Table 4: Ablation Seam-Awar Padding in the spherical neural field. KID* values are re- ported in units of 10-2.
SR Blocks
Reconstruction
Generation
PSNRT LPIPS
FID
KID'J CLIP-FID
SAP
Reconstruction
Generation
28.65
0.2205
27.93
0.99
3.03
PSNRT LPIPS]
FID KID' CLIP-FID
30.07
0.1680
25.49
0.97
2.94
X 29.17
0.1991
27.69
1.04
3.06
V
30.07
0.1680
25.49
0.97
2.94
employs a super-resolution backbone to estimate local textures, which then modulated by locally varying Jacobian matrices of coordinate transformations to predict Fourier responses (Tancik et al., 2020). This design improves the model's ability to handle geometric transformations such homog- raphy and ERP-to-perspectivv projection. Across all tested resolutions, our spherical neural field achieves consistently lower patchFID (Chai et al., 2022) (pFID) compared to the LTEW-based base- line, demonstrating clear advantages in fidelity, scalability, and robustness for arbitrary and perspective image generation.
Ablation Spherical Residual Blocks We ablate the proposed spherical residual blocks panoramic reconstruction and generation. Tab. 3 shows that removing spherical residual blocks lowers PSNR and raises LPIPS, indicating degraded fidelity, and also increases FID, KID, and Clip- FID, reflecting poorer realism. This confirms that spherical residual blocks are critical for geometry- aware feature extraction and maintaining consistency across resolutions.
Ablation on Seam-aware Padding We evaluate the contribution of Seam-Aware Padding (SAP) by removing it separately in two components. Specifically, Tab. 4 reports results when removing SAP from the spherical neural field, while keeping it in the diffusion UNet. This leads to clear degradation FID, KID, and CLIP-FID, confirming the necessity of SAP during spherical decoding. In addition, Fig. 6 shows qualitative comparisons when removing SAP from the diffusion UNet. Without SAP, visible seams and semantic misalignment appear at patch boundaries, whereas the full design produces seamless and consistent panoramas.
6 CONCLUTION
We introduced ASIG, the first diffusion-based framework that unifies controllable generation of viewpoint, FOV, and resolution across diverse image shapes. By combining mesh-based spherical latent diffusion for scene representation with spherical neural field for distortion-free sampling, ASIG enables high-quality, ecometryy-onsissen image synthesis across diverse shapes. Experi- ments demonstrate that ASIG achieves superior results perspective, panoramic, and fisheye im- ages, surpassing specialized baselines. Moreover, ASIG generalizes well to out-of-domall scenes, highlighting its robustness.

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ACKNOWLEDGEMENTS
This work was supported in part by the National Natural Science Foundation of China under Grant 62131003, and the Fundamental Research Funds for the Central Universities under Grant WK2100000059.
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APPENDIX
A OUT-OF-DOMAIN GENERATION
We further generate 200 prompts for each Out-of-domain (OOD) scene type, covering a wide range of scene types, and compare model against both SOTA panoramic generation methods (Zhang et al., 2024; Sun et al., 2025) and vanilla SDXL (Podell et al., 2023) on the CLIP-Score (CS) (Rad- ford et al., 2021) metric. As shown in Tab. 5, our OOD CS metric remain close to vanilla SDXL and outperform SOTA panoramic baselines. We also assess generation quality (Wu et al., 2023; Mittal et al., 2012; 2011) across these scenes. As shown in Tab. 6, no performance gap under ID and OOD prompts condition; in all scenes, our method surpasses competing panoramic methods. Overall, these experiments confirm that our model retains strong open-domain generation capability while enabling arbitrary shape generation.
In Figs. and we present additional OOD visual results, including both panoramas and perspective views, which consistently demonstrate high quality and rich details. In Fig. 9, we provide additional OOD visual results across diverse scene types, further illustrating that our method maintains high visual quality even on OOD scenes.
B MESH-BASED SPHERICAL RECONSTRUCTION PERFORMANCE AND COMPONENT OVERHEAD.
Our spherical representation has clear advantages over ERP and cubemap (Kalischek et al., 2025). ERP suffers from severe polar distortion, while cubemap introduces discontinuities and uneven sampling. In contrast, our mesh-based sphrical representation provides uniform sampling over the sphere, enabling the spherical neural field to behave consistently across viewpoints. As result, obtain more coherent geometry and better reconstruction quality. Tab. shows higher PSNR and lower LPIPS (Zhang et al., 2018) than both baselines.
Moreover, the Seam-Aware Padding (SAP) tailored for spherical representations introduces no addi- tional FLOPs and only minimal latency, since it consists solely of lightweight indexing and copying. As shown in the Tab. 8, our comparison between UNet with and without SAP indicates only a small runtime difference during inference (measured A 110)). Importantly, once the spherical latent rep- resentation is generated, the spherical neural field (SNF) renders images at just 0.14s per image at 768x1024 resolution, while still supporting arbitrary viewpoints, FOVs, resolutions, and projection types. This demonstrates that the overall system remains efficient even with SAP.
C FURTHER EXTENSIONS ON NEURAL FIELD TRAINING
We incorporated perspective images from the LSDIR (Li et al., 2023b) dataset into the training of our spherical neural field. Each perspective image randomly projected onto our mesh-based
Table 5: Comparison of CLIP-Score (Radford Table 6: Comparison of no-reference metrics on ID and OOD scenes.
et al., 2021) across various OOD scenes.
OOD Scene Nature Fantasy Weather City Complex Avg.
Method
NIQEL
BRISQUEL
QAquiiv QAasstitc
PanFusion
32.33
32.81
30.97
30.97
30.01
31.14
ID OOD 00D ID OOD
ID
OOD
SMGD
23.77
26.30
22.22
24.83
28.46
25.12
PanFusion
4.74 4.92 38.13 38.06 3,99 3,87 3,51 3,71
SDXL Venita
32.98
33.43
32.51
31.39
31.05
32.27
SMGD
4.12 4.23 33.27
33.41 4.03
3,96
3.22
3.45
Ours
32.84
33.23
31.23
31.02
30.71
31.81
Ours
3,08 3.18 18.02
18.15
4.40 4.31 3.97 4.05
Table Comparison of differ- ent panoramic representations son of UNet with and without across both reconstruction and gen- for reconstruction quality.
Table 8: Runtime compari- Table 9: Comparison of performance
SAP, and SNF rendering time. eration tasks.
Method
PSNRt
LPIPS
Component
(8)
Train Data
Reconstruction
Generation
ERP 29.07
0.1823
UNet w/o SAP
3.23
PSNRT LLPPS FID KIDU
Cubemap
29.42
0.1788
UNet w SAP
3.62
MattrrportD
30.07 / 061668
/.997 25.22 0.94
Ours
30.07
0.1680
SNF
0.14
L.SSIR
30.36 06155
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spherical representation, and the spherical neural field is trained reconstruct these images. As shown in Tab. 9, evaluations on both reconstruction and generation tasks show clear performance improvements compared to training only Cathe et al., 2017).
D MORE VISUAL COMPARISON WITH BASELINE METHODS
In Fig. 10, show further visual comparisons with baselines. ASIG consistently outperforms them on both perspective and panorama generation, delivering richer details, more realistic textures, and uniform image quality.
E ADDITIONAL VISUAL RESULTS ON SD3/DIT BACKBONES
In Fig. 11, present additional visual results produced by SD3/DiT-based (Esser al., 2024) models. These samples demonstrate excellent texture quality and color fidelity, further showing that method is transferable across different diffusion backbones.
F THE USE OF LARGE LANGUAGE MODELS
We employed Qwen-3 refine sentence clarity and check spelling. No large language model was employed for generating experimental results, figures, core technical claims. All content was reviewed, verified, and approved by the human authors.
G ETHICS STATEMENT
We confirm that this work adheres the ICLR Code of Ethics. It does involve human subjects, sensitive data, or practices that raise ethical concerns.
H REPRODUCIBILITY STATEMENT
We detail the components of our method in Sec. and the experimental settings in Sec. 4, and pro- vide extensive visual results in Sec. and the Appendix for verification. We will release the training and testing code together with the processed datasets upon publication to ensure full reproducibility.
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Figure 7: Out-offdommin examples (panoramas and perspective views) generated by ASIG with the text prompt: "A photo of house by tranquil lake, with calm water, bright daylight."
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Figure 8: Out-of-domain examples (panoramas and perspective views) generated by ASIG with the text prompt: "A photo of forest and skyscrapers, with lake under sunny white clouds."
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Natute Prompt: photo of panoramic mountain landscape sunrise with misty vulleys and tall trees.
Natute Prompt: photo of desert panorama with plants under dramatic cloudy sky.
Fantasy Prompt: a photo fantasy forest panorama with glowing giant mushrooms colorful lights.
Fantasy Prompt: a photo fairytale village panorama with wooden cottages warn sunlight.
Weather Prompt: photo of snow-covered panoramic field under calm sky.
Weather Prompt: photo of forest panorama during thunderstorm with bright lightning and wet foliage.
City Prompt: photo of harbor city panorama sunset with yachts, bridges, and golden reflections.
City Prompt: photo of an urban waterfront panorama with skyserapers rising above busy marina.
Figure 9: Out-of-domain examples covering diverse outdoor scene types (nature, fantasy. weather, and city). Although these scenes are far from the training domain (indoor Matterport3D), our method produces high-quality and semantically coherent panoramic generations.
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Perspective
Penspective
pamo
SOXI.
vyDiifation
Pann aniion
SMGD
Perspective View
Zoomed
Perspective View
Zoomed
Panoramic View
Zoomed
SDXI paso
SMGD
GT
Figure 10: Qualitative comparison with baseline methods on perspective and panorama generation.
ASIG produces richer details, more realistic textures, and more consistent image quality across views.
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ad
Figure 11: Visual results generated by the DiT-based model, showing high-quality textures, consis- tent colors, and overall strong visual fidelity.
19


Reviews:
[{"review_id": "mgGNdiLkjJ", "reviewer": "Reviewer_mcAc", "summary": "This paper presents a method for generating images with arbitrary shapes (different aspect ratios, etc.) and projections (e.g. panoramic, fisheye) form a single model. It defines a 2D latent diffusion over a sphere around the camera, and rasterises from this to create the final image. Standard U-net architectures are applied on the sphere by discretising it as a modified icosahedron, subdividing triangles, and noting that pairs of triangles form diamonds having a grid topology. Beyond this the method is a standard latent diffusion. The model is initialised from SDXL, and fine-tuned on the Matterport3D dataset.", "questions": "Please address the points mentioned under \"Weaknesses\" above – in particular, measure more thoroughly how well open-domain generation works with the fine-tuned model.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The proposed approach is novel; latent diffusion over a discretised sphere is an elegant approach to learning a generative model over 2D visual content that is invariant to the camera projection type and parameters. The specific approach to mapping the sphere to grid topology (so pretrained UNets) can be used is also interesting.\n\nResults are shown using a single model to generate diverse images – perspective at diverse aspect ratios, panoramic, and fisheye. Quality is comparable for all image types, with no visible degradation e.g. in highly deformed regions.\n\nCompared with several baselines for panoramic/large/multiview image generation, the proposed method achieves stronger results for three projection types across seven metrics; FID in particular is significantly lower than any other methods.\n\nThere is a fairly detailed ablation study, measuring the benefit of several of the introduced components, including the spherical residual block, and use of a spherical representation instead of direct deformation of planar generations.\n\nThe model is shown to retain some of the open-domain characteristics of the original SDXL model it is fine-tuned from (despite that fine-tuning only using the single-domain Matterport3D dataset) – in particular, it is still possible to generate out-of-distribution images given text conditioning, even under fisheye/etc projections.", "weaknesses": "The overall technical innovation is rather small. The tricks required to apply the diffusion UNet on the sphere are not particularly noteworthy (e.g. there is significant discussion of what simply amounts to circular convolution). Beyond this, the model is essentially a standard latent diffusion model.\n\nIt is unclear how far the ability to generate arbitrary scenes (as opposed to those in-distribution for Matterport3D) is retained. While one qualitative example is shown, in addition there should be a quantitative comparison (e.g. using CLIP score) on a broad range of prompts, and this compared against the vanilla SDXL.\n\nThe claim of \"arbitrary resolution\" is rather strong. The method is trained using a fixed discretisation of the sphere, and while this can be sampled at arbitrarily high spatial frequencies, the fineness of synthesised details is bounded by the discretisation (and the training data) – unlike truly \"infinite resolution\" models like Generative Powers of Ten. The present work is similar to training a very high resolution 2D diffusion then cropping out an ROI of the desired resolution (though of course the spherical representation allows other lens types etc.).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TP2lXyfwy3", "reviewer": "Reviewer_73SH", "summary": "This paper proposes ASIG, a novel diffusion framework designed to overcome the limitations of existing models by enabling explicit control over viewpoint, FOV, and resolution in a unified system. The method first generates a complete spherical scene representation via a mesh-based latent diffusion, and then uses a spherical neural field to sample and render arbitrary-shaped images (e.g., perspective, panoramic). The quantitative and qualitative results shows clear improvements over current methods.", "questions": "1. Why the 'Receptive Field Remap' is needed, given that the VAE encoder already processes features using 'SAP'?\n\n2. Where do the weights of the VAE encoder come from? Are the other models that need to be trained trained from scratch?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. ASIG enables explicit control over viewpoint, FOV, and resolution within a unified system. Both quantitative and qualitative results confirm that the method outperforms existing approaches. \n\n2. The authors' methodology is reasonable. ASIG constructs a spherical latent space by linking UNet principles with an $L$-subdivided icosahedron, and the proposed seam-aware padding (SAP) and spherical neural field components are effective in achieving excellent results.", "weaknesses": "1. Some ablation results are confusing.\n\n\ta. The reported results for ASIG in Table 2 are inconsistent with the corresponding results in Table 1.\n\n\tb. Furthermore, the ASIG results presented in Table 2 also differ from those in Table 3 and Table 4.\n\n2. The Sampler component is underspecified.\n\n\ta. The Sampler appears to integrate two distinct information sources: a 'remapped mesh-structure' and 'information processed by SAP'. It is unclear how these sources are fused or prioritized. Is there conflict between them and how they merge together?\n\n\tb. The process for inputting the 'sampling region' information is not detailed. Please elaborate on how this region is defined and fed into the sampler.\n\n3. The manuscript's clarity needs significant improvement, as several sections are difficult to understand.\n\n\ta. The explanation for Equation 3 + line 204 is insufficient. The authors should provide a more detailed derivation or justification.\n\n\tb. The description accompanying Equation 5 (line 248), particularly the phrase \"F guided by...\" (or \"F guided by $\\pi$\"?), is ambiguous. It is not clear what this operation entails or how the guidance mechanism works.\n\n4. Some important details are missing.\n\n\ta. Whether the sampling strategy requires an additional stitching mechanism or post-processing step to ensure seamless transitions between different sampled patches?\n\n\tb. In SAP component, transforming rectangular inputs into squares is unclear. How is this achieved (e.g., resizing?), and does this step introduce unwanted distortion?\n\n\tc. What is the meaning of \"out-of-distribution\" (OOD) (Figure 14&15).\n\n\td. How to apply different projection functions π(·)?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "RnBglpSuec", "reviewer": "Reviewer_L4Gf", "summary": "This paper introduces ASIG, a diffusion-based framework for arbitrary-shaped image generation, which unifies viewpoint, field-of-view (FOV), and resolution control within a single model. ASIG combines two main components: (1) Mesh-based spherical latent diffusion representing the scene over a subdivided icosahedron mesh with a seam-aware padding (SAP) mechanism to ensure spatial consistency. (2) Spherical neural field (SNF) – decoding the spherical latent representation into images with arbitrary projections (perspective, panoramic, fisheye) via coordinate-conditioned sampling.", "questions": "See weakness.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. Solid Technical Design. The authors designed a new representation for the spherical diffusion model and achieved arbitrary viewpoint and resolution generation through improved VAE.\n\n2. The unification of perspective, panorama, and fisheye generation in one framework is interesting.", "weaknesses": "1. Seam-aware padding is a clever design, but it seems to significantly increase the complexity of the model during computation.\n\n2. Although SAP and SR blocks are ablated, the contribution of the mesh subdivision is not studied. One thing I'm curious about is what advantages this representation method has compared to many commonly used Cube representation methods? It seems that many of the claimed advantages are due to the addition of control signals in the VAE decoding process, rather than this representation method itself.\n\n3. Lack of comparison with some of the latest methods, such as LayerPano3d[1] and PAR[2] :\n\n[1] LayerPano3D: Layered 3D Panorama for Hyper-Immersive Scene Generation\n[2] Conditional panoramic image generation via masked autoregressive modeling", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CejcQ32MM4", "reviewer": "Reviewer_6hfe", "summary": "This paper proposes Arbitrary-Shaped Image Generation (ASIG), whose core idea is to use the latent of an implicit field representation as the denoising target, enabling resolution- and field-of-view-independent image generation. The method introduces Mesh-based Spherical Latent, Seam-aware Padding, and Seam Enforcement Denoising for the design of the diffusion representation, model, and network architecture.", "questions": "Please see the weaknesses.\n\nOverall, my assessment of this paper is positive. I look forward to the authors’ responses to my concerns and remain open to the possibility of raising my score.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper proposes an interesting framework for resolution-independent image generation. Both the paper and the framework are easy to follow and understand.\n2. Although using implicit representations to handle varying resolutions is an effective and commonly adopted approach, the representation, network, and model design proposed in this paper are well-motivated and meaningful.\n3. The paper provides detailed and comprehensive quantitative experiments to demonstrate the effectiveness of the model, achieving state-of-the-art performance on multiple tasks, including panoramic, perspective, and fisheye image generation.", "weaknesses": "1. Considering that DiT-based architectures are currently more mainstream and have demonstrated superior performance, using a U-Net–style architecture might be somewhat outdated. The authors could further explore attention-based operators under their proposed representation to enrich the paper’s content and potentially improve model performance.\n2. One major advantage of implicit representations, beyond enabling resolution-independent inference, is that during training they could also leverage diverse types of images (e.g., standard perspective images) to augment the dataset and improve model generalization. The paper currently lacks exploration in this direction, but I believe this could be a promising avenue for further performance improvement.\n3. Although the operators designed for the spherical grid are reasonable, and the authors cleverly convert them into regular tensors, I question how practical these methods are for real-world implementation and deployment, especially since the authors have not released the model code.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)