SpheRoPE：基於球面RoPE的零樣本無優化360度全景生成

摘要

我們提出了一種零樣本、無需訓練且無需優化的框架，透過將球面先驗直接注入預訓練的擴散變換器中，生成360度全景圖像和影片。現有方法要麼依賴對稀缺全景數據進行代價高昂的微調，從而限制了泛化能力；要麼利用多步優化，導致推理延遲過高。我們觀察到當代生成模型從大規模訓練中自然展現出一些全景先驗。然而，這些湧現的能力並不充分，因為模型從根本上無法滿足等距柱狀投影（ERP）施加的嚴格拓撲約束。我們提出了一種零樣本且無需優化的方法，在推理時解決這些約束。球形RoPE取代了標準旋轉位置編碼：低頻通道被重新參數化為3D笛卡爾坐標，以天然編碼球面流形，而高頻通道則被諧波量化以強制實現精確週期性。結合明確引導幾何結構的互補性語義失真無分類器引導（CFG），我們避免了重新訓練，並繼承了最先進模型的全部創作廣度。我們的方法適用於不同的骨幹網絡和360度生成模態。我們透過使用Flux.1、Flux.2和LTX-Video骨幹網絡，在文字轉全景任務上展示了這一點，在無需訓練的情況下實現了與基線相競爭的績效。專案頁面：https://orhir.github.io/SpheRoPE

English

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