OpenNovelty.org

Arbitrary-Shaped Image Generation via Spherical Neural Field Diffusion

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Diffusion ModelsImage GenerationSpherical Neural Field
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.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes ASIG, a framework for generating images with arbitrary shapes (perspective, panoramic, fisheye) while controlling spatial attributes like viewpoint, field-of-view, and resolution. Within the taxonomy, it occupies the 'Spherical Neural Field Diffusion' leaf under 'Spherical and Non-Rectangular Image Representations'. Notably, this leaf contains only the original paper itself—no sibling papers exist in this specific category. This positioning suggests the work addresses a relatively sparse research direction focused on spherical representations combined with neural field diffusion, distinct from the more populated branches handling rectangular layouts or mesh-based irregular shapes.

The taxonomy reveals neighboring work in 'Irregular Shape and Mesh-Based Generation' (2 papers) and broader branches like 'Spatial Layout and Regional Control Mechanisms' (9 papers) and 'Shape-Guided and Geometry-Aware Generation' (8 papers). The scope note for the original paper's leaf explicitly excludes non-spherical arbitrary shape methods, while the parent category excludes standard rectangular generation. This boundary-setting indicates the work diverges from conventional layout-based control (e.g., Migc, ReCo) and sketch-guided synthesis, instead pursuing continuous spherical parameterizations. The taxonomy structure shows most prior work concentrates on rectangular formats or discrete spatial grids rather than spherical coordinate systems.

Among 21 candidates examined across three contributions, no refutable prior work was identified. The ASIG framework contribution examined 10 candidates with zero refutations; mesh-based spherical latent diffusion examined 1 candidate (also zero refutations); and spherical neural field sampling examined 10 candidates with no overlapping prior work. This limited search scope—top-K semantic matches plus citation expansion—suggests that within the examined literature, the specific combination of spherical latent diffusion, seam enforcement denoising, and coordinate-conditioned neural field sampling appears relatively unexplored. However, the small candidate pool (21 total) means substantial related work may exist outside this search radius.

Based on the limited search covering 21 candidates, the work appears to occupy a sparse niche combining spherical representations with neural field diffusion for arbitrary-shaped generation. The taxonomy's single-paper leaf and absence of refutable candidates suggest novelty in this specific technical combination, though the restricted search scope prevents definitive claims about the broader literature landscape. The analysis captures top semantic matches but does not constitute exhaustive coverage of panoramic generation, neural fields, or diffusion-based spatial control methods.

Taxonomy

Core-task Taxonomy Papers
36
3
Claimed Contributions
21
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: arbitrary-shaped image generation with spatial attribute control. The field encompasses diverse approaches to generating images beyond standard rectangular formats while maintaining fine-grained control over spatial attributes and content placement. The taxonomy reveals several major branches: Spatial Layout and Regional Control Mechanisms focus on precise positioning and attribute assignment within defined regions, exemplified by works like Migc[3] and ReCo[12]. Shape-Guided and Geometry-Aware Generation emphasizes using explicit geometric cues such as sketches or shape priors to steer synthesis, as seen in Sketch-Guided Generation[2] and Shape-conditioned Generation[22]. Spherical and Non-Rectangular Image Representations address non-standard canvas geometries including panoramic and spherical formats. Internal Representation Guidance and Self-Guidance explores leveraging learned features within generative models themselves, illustrated by Diffusion Self-Guidance[4]. Attribute-Specific and Domain-Specialized Control targets particular domains or modalities, while Shape Prediction and Reconstruction from Projections handles inverse problems, and Peripheral and Supporting Technologies provides foundational tools. Recent activity highlights contrasting strategies for achieving spatial control: some methods impose layout constraints through attention mechanisms or regional conditioning, while others embed geometric awareness directly into neural representations. Trade-offs emerge between flexibility in shape specification and computational efficiency, as well as between explicit geometric guidance and learned implicit control. Spherical Neural Field[0] sits within the Spherical and Non-Rectangular Image Representations branch, addressing the challenge of generating content on non-planar surfaces using neural field representations combined with diffusion processes. This approach contrasts with more conventional rectangular layout methods like Migc[3] or sketch-based techniques such as Sketch-Guided Generation[2], emphasizing continuous spherical parameterizations rather than discrete spatial grids. The work reflects growing interest in extending generative models beyond traditional image formats to handle arbitrary topologies and coordinate systems.

Claimed Contributions

ASIG: Arbitrary-Shaped Image Generation framework

ASIG is the first unified diffusion-based framework that enables explicit control over viewpoint, field-of-view, and resolution while supporting generation of diverse image shapes including perspective, panoramic, and fisheye images. It addresses the limitation of existing methods that remain confined to fixed image shapes and lack flexible spatial attribute control.

10 retrieved papers
Mesh-based spherical latent diffusion with seam enforcement denoising

The authors propose a mesh-based spherical latent diffusion built on a multi-level subdivided icosahedron that generates complete scene representations. It incorporates a seam enforcement denoising strategy using Seam-Aware Padding to maintain semantic and spatial consistency across different viewpoints and patch boundaries.

1 retrieved paper
Spherical neural field for coordinate-conditioned sampling

The authors introduce a spherical neural field that incorporates feature extraction modules aligned with spherical topology. It enables coordinate-conditioned, distortion-free sampling of arbitrary regions at flexible resolutions, allowing precise control over spatial attributes while maintaining high generation quality.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

ASIG: Arbitrary-Shaped Image Generation framework

ASIG is the first unified diffusion-based framework that enables explicit control over viewpoint, field-of-view, and resolution while supporting generation of diverse image shapes including perspective, panoramic, and fisheye images. It addresses the limitation of existing methods that remain confined to fixed image shapes and lack flexible spatial attribute control.

Contribution

Mesh-based spherical latent diffusion with seam enforcement denoising

The authors propose a mesh-based spherical latent diffusion built on a multi-level subdivided icosahedron that generates complete scene representations. It incorporates a seam enforcement denoising strategy using Seam-Aware Padding to maintain semantic and spatial consistency across different viewpoints and patch boundaries.

Contribution

Spherical neural field for coordinate-conditioned sampling

The authors introduce a spherical neural field that incorporates feature extraction modules aligned with spherical topology. It enables coordinate-conditioned, distortion-free sampling of arbitrary regions at flexible resolutions, allowing precise control over spatial attributes while maintaining high generation quality.

Table of Contents