| title | SpheRoPE: Zero-Shot Optimization-Free 360 Panorama Generation with Spherical RoPE |
|---|---|
| authors | Or Hirschorn, Aaron Olender, Eli Alshan, Ianir Ideses, Lior Fritz, Sagie Benaim |
| date | 2026-06-30 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.32033 |
| upvotes | 6 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | 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 |
cite
@misc{hirschorn2026spherope,
title={SpheRoPE: Zero-Shot Optimization-Free 360 Panorama Generation with Spherical RoPE},
author={Or Hirschorn and Aaron Olender and Eli Alshan and Ianir Ideses and Lior Fritz and Sagie Benaim},
year={2026},
eprint={2606.32033},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.32033}
}