Gimbal360: Canonicalizing Planar Diffusion for Spherical Panorama Completion

Yuqin Lu; Haofeng Liu; Yang Zhou; Yihua Dai; Guiqing Li; Shengfeng He; Jun Liang

cs.CVMar 24, 2026

Gimbal360: Canonicalizing Planar Diffusion for Spherical Panorama Completion

Authors: Yuqin Lu, Haofeng Liu, Yang Zhou, Yihua Dai, Guiqing Li, Shengfeng He, Jun Liang

Abstract

Diffusion models provide powerful priors for 2D image completion, but these priors are learned on bounded planar images and do not transfer directly to $360^\circ$ panoramas. Perspective observations and spherical panoramas differ in both projective geometry and topology: viewpoint-dependent distortion complicates spatial correspondence, while Equirectangular Projection (ERP) panoramas exhibit intrinsic $S^1$ periodicity that standard Euclidean architectures do not preserve. We present Gimbal360, a unified framework that adapts planar diffusion priors to spherical panoramic completion by standardizing these geometric and topological structures. Our Canonical Viewing Space expresses projective distortion as a fixed function of latitude, providing a consistent interface between perspective inputs and spherical panoramas. To map unposed in-the-wild images into this space, Differentiable Projective Canonicalization projects a dense correspondence field onto a 3-DoF rigid projection manifold without requiring camera parameters at inference. We further introduce Topologically Equivariant Generation, which enforces latent shift equivariance to preserve continuity across the periodic ERP boundary. Together, these designs allow diffusion to operate in a representation whose geometry and topology are explicitly aligned with the spherical domain. We also introduce Horizon360, a curated large-scale dataset of gravity-aligned panoramic environments. Extensive experiments show that Gimbal360 achieves state-of-the-art visual fidelity and seam continuity in $360^\circ$ scene completion.

Figures & tables

Figure 2: Overview of Gimbal360. Perspective views are sampled from Horizon360, our gravity-aligned canonical structural prior (top left). Differentiable Projective Canonicalization (DPC, bottom left) feeds the perspective input through a geometric encoder to predict a dense correspondence field, which the rigid solver collapses into a rigid coordinate grid that warps the view into the Canonical Viewing Space. Topologically Equivariant Generation (TEG, right) denoises in this space, where circular latent encoding wraps the frozen VAE via circular-padding and cropping, and a Siamese consistency loss aligns the predictions of the base and circularly shifted streams of a shared DiT. Together these mechanisms teach the generator to respect the periodic $S^{1}$ boundary (bottom right), ensuring structurally consistent and seamless panorama completions.

	Indoor, 5K						Outdoor, 5K
Method	FID $\downarrow$	KID( $\times 10^{2}$ ) $\downarrow$	O-FID $\downarrow$	FAED $\downarrow$	CS $\uparrow$	DS $\downarrow$	FID $\downarrow$	KID( $\times 10^{2}$ ) $\downarrow$	O-FID $\downarrow$	FAED $\downarrow$	CS $\uparrow$	DS $\downarrow$
HunyuanWorld 1.0	29.23	2.1981	53.26	4.05	26.48	0.92	41.37	2.2218	69.44	7.07	29.13	1.00
WorldGen	27.43	1.9683	50.50	2.45	26.44	1.32	39.78	2.0616	67.47	6.47	29.10	1.01
DiT360	38.43	3.4848	70.61	11.30	31.09	3.76	55.17	3.7295	65.56	8.35	31.40	2.08
Ours	15.92	1.0249	30.10	1.36	30.68	0.49	23.78	0.9228	47.48	1.75	31.29	0.66

Table 1: Quantitative comparison on both indoor and outdoor scenes, with 5,000 held-out samples per domain. KID is scaled by

10^{2}

. Bold marks the best result.

Figure 3: Qualitative comparison on indoor (left) and outdoor (right) scenes. Baseline completions exhibit warped structures, mismatched distortion profiles, and boundary discontinuities, whereas ours completes structurally coherent, boundary-continuous panoramas. Please zoom in for details.

Figure 4: Qualitative results of in-the-wild generalization. Inputs include real-world captures and styled photos. Competing methods produce mismatched surroundings or warped layouts, whereas Gimbal360 completes structure- and style-consistent panoramas. Please zoom in for details.

	Indoor, 0.5K				Outdoor, 0.5K
Method	FID $\downarrow$	KID $\downarrow$	O-FID $\downarrow$	DS $\downarrow$	FID $\downarrow$	KID $\downarrow$	O-FID $\downarrow$	DS $\downarrow$
w/o DPC	64.48	1.2716	93.03	1.63	69.82	1.9039	108.00	1.09
Geocalib	70.50	1.3122	100.27	1.10	78.93	2.1227	112.35	0.98
PF	72.54	1.3640	102.36	0.99	81.25	2.5321	116.56	0.94
w/o TEG	55.05	0.8106	79.49	1.75	62.74	0.8194	106.62	1.49
Ours	54.18	0.8012	79.45	0.51	63.28	0.8236	99.04	0.69

Table 2: Ablation study and anchoring baselines. Geocalib and PF denote replacing DPC with GeoCalib [ 43 ] and PerspectiveFields [ 21 ] , respectively, while keeping TEG fixed. Bold marks the best result. KID is scaled by

10^{2}

Figure 5: Qualitative ablation results. Removing DPC yields severe structural distortion while removing TEG breaks continuity near the periodic boundary. Our full model produces structurally coherent and boundary-continuous completions.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 6: Horizon360 Dataset Diversity. A representative sample of our curated, gravity-aligned panoramas. The dataset spans a wide domain of indoor structures and outdoor landscapes under diverse lighting conditions, providing a robust, generalized training signal for the Gimbal360 framework.

Figure 7: Dataset Canonicalization. Visualization of the rectification process applied to Horizon360. Raw unaligned panoramas (left) exhibiting severe sinusoidal distortions are mathematically rotated into the Canonical Viewing Space (right), ensuring a perfectly straight equator and plumb architectural verticals.

Figure 8: Out-of-Distribution Generalization.

Figure 9: Out-of-Distribution Generalization.

Figure 10: Out-of-Distribution Generalization.

Figure 11: Boundary Continuity Comparison. A zoomed-in analysis of the $S^{1}$ azimuthal seam. Baseline method (left) relying on inference-time circular blending suffer from unnatural texture smoothing and relocated secondary seams at the blend edges. Gimbal360 (right) natively generates seamless boundaries with preserved high-frequency details and structural integrity.

Figure 12: Limitations and Failure Cases. (Top) Generation with humans leads to inaccurate anatomical distortions. (Bottom) An extreme telephoto input (right) provides insufficient context, occasionally resulting in completion with less coherent global geometry than a wide-angled input (left).

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Open paper AskPDF arXiv

We present a zero-shot, training-free and optimization-free framework for generating 360 panoramic images and videos by directly injecting spherical priors into pre-trained diffusion transformers. Existing methods either rely on costly fine-tuning on scarce panoramic data that limits generalization, or leverage multi-step optimization that incurs prohibitive inference latency. We observe that contemporary generative models natively exhibit some panoramic priors from large-scale training. However, these emergent capabilities are insufficient, as the models fundamentally fail to satisfy the rigorous topological constraints imposed by equirectangular projection (ERP). We introduce a zero-shot and optimization-free approach that resolves these constraints at inference time. Spherical RoPE replaces standard rotary position embeddings: low-frequency channels are re-parameterized as 3D Cartesian coordinates to natively encode the spherical manifold, while high-frequency channels are harmonically quantized to enforce exact periodicity. Coupled with complementary Semantic Distortion classifier-free guidance (CFG) that explicitly steers geometry, we avoid retraining and inherit the full creative breadth of state-of-the-art models. Our approach generalizes across diverse backbones and 360 generation modalities. We demonstrate this across text-to-panorama using Flux.1, Flux.2, and LTX-Video backbones, achieving competitive performance against baselines, all while remaining training-free. Project page: https://orhir.github.io/SpheRoPE

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