SpheRoPE: Zero-Shot Optimization-Free 360 Panorama Generation with Spherical RoPE

• URL: http://arxiv.org/abs/2606.32033v1
• Date: Tue, 30 Jun 2026 17:57:04 GMT
• Title: SpheRoPE: Zero-Shot Optimization-Free 360 Panorama Generation with Spherical RoPE
• Abstract summary: We present a framework for generating 360 panoramic images and videos by directly injecting spherical priors into pre-trained diffusion transformers.<n>Our approach generalizes across text-to-panorama using Flux.1, Flux.2, and LTX-Video backbones, achieving competitive performance against baselines.
• Score: 12.307239950961446
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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

Related papers

• Compact Neural Appearance Models for Efficient Gaussian Splatting [1.579851447355843]
  primitive-based radiance fields such as 3D Gaussian Splatting typically model view-dependent appearance using low-order spherical harmonics (SH)<n>We present a thorough, end-to-end comparison of SH and recent spherical appearance models.<n>We introduce an implicit alternative that decodes compact per-primitive latent codes using a tiny shared pipeline.
  arXiv  Detail & Related papers  (2026-09-04T15:18:54Z)
• GeoFlow: Efficient Driving Video Generation via Geometry-Aligned Priors [27.10799700736534]
  Generative models like Diffusion Models and Flow Matching have demonstrated remarkable capabilities in synthesizing high-fidelity driving videos.<n>We argue that this inefficiency stems from the prevailing reliance on a standard Gaussian source distribution, where consecutive frames are as independent Gaussian noise.<n>We propose GeoFlow, a novel framework designed to achieve efficient driving video generation by harnessing explicit geometric priors.
  arXiv  Detail & Related papers  (2026-08-12T15:57:08Z)
• Improving Sparse-View 3DGS Generalization via Flat Minima Optimization [37.65568887478836]
  Recent advances in neural rendering have established 3D Gaussian Splatting (3DGS) as a highly efficient representation for novel view synthesis.<n>However, when supervision is limited to sparse input views, 3DGS tends to overfit to the observed images and generalize poorly to unseen viewpoints.<n>We address this challenge from the perspective of flat minima (FM) optimization, which seeks solutions that remain stable under small parameter perturbations.
  arXiv  Detail & Related papers  (2026-07-01T12:52:57Z)
• Spherical-to-ERP Epipolar Rectification for Single-Axis Disparity in 360 Stereo [0.0]
  We adopt a standard spherical-to-equirectangular (ERP) projection as a preprocessing step, which straightens epipolar curves and restores a one-dimensional disparity structure.<n>Experiments on synthetic fisheye stereo datasets show that this spherical-to-ERP-to-RAFT+EACS pipeline produces accurate, smooth, and structurally consistent disparity maps at real-time speed.
  arXiv  Detail & Related papers  (2026-06-23T17:27:07Z)
• ViPS: Video-informed Pose Spaces for Auto-Rigged Meshes [55.32681167870698]
  Video-informed Pose Spaces (ViPS) is a feed-forward framework that discovers the latent distribution of valid articulations for auto-rigged meshes.<n>ViPS transfers generative video priors into a universal distribution over a given rig parameterization.<n>Our evaluations show that ViPS matches the performance of state-of-the-art methods trained on synthetic artist-created 4D data in both plausibility and diversity.
  arXiv  Detail & Related papers  (2026-04-19T21:21:11Z)
• Spherical-GOF: Geometry-Aware Panoramic Gaussian Opacity Fields for 3D Scene Reconstruction [17.20174890101058]
  We present Spherical-GOF, an omnidirectional rendering framework built upon Gaussian Opacity Fields (GOF)<n>GOF performs GOF ray sampling directly on the unit sphere in spherical space, enabling consistent ray-Gaussian interactions for panoramic rendering.<n>Experiments on standard panoramic benchmarks demonstrate competitive photometric quality and substantially improved consistency.
  arXiv  Detail & Related papers  (2026-03-09T15:35:56Z)
• TALO: Pushing 3D Vision Foundation Models Towards Globally Consistent Online Reconstruction [57.46712611558817]
  3D vision foundation models have shown strong generalization in reconstructing key 3D attributes from uncalibrated images through a single feed-forward pass.<n>Recent strategies align consecutive predictions by solving global transformation, yet our analysis reveals their fundamental limitations in assumption validity, local alignment scope, and robustness under noisy geometry.<n>We propose a higher-DOF and long-term alignment framework based on Thin Plate Spline, leveraging globally propagated control points to correct spatially varying inconsistencies.
  arXiv  Detail & Related papers  (2025-12-02T02:22:20Z)
• OracleGS: Grounding Generative Priors for Sparse-View Gaussian Splatting [78.70702961852119]
  OracleGS reconciles generative completeness with regressive fidelity for sparse view Gaussian Splatting.<n>Our approach conditions the powerful generative prior on multi-view geometric evidence, filtering hallucinatory artifacts while preserving plausible completions in under-constrained regions.
  arXiv  Detail & Related papers  (2025-09-27T11:19:32Z)
• HoliGS: Holistic Gaussian Splatting for Embodied View Synthesis [59.25751939710903]
  We propose a novel deformable Gaussian splatting framework that addresses embodied view synthesis from long monocular RGB videos.<n>Our method leverages invertible Gaussian Splatting deformation networks to reconstruct large-scale, dynamic environments accurately.<n>Results highlight a practical and scalable solution for EVS in real-world scenarios.
  arXiv  Detail & Related papers  (2025-06-24T03:54:40Z)
• SphereDiff: Tuning-free Omnidirectional Panoramic Image and Video Generation via Spherical Latent Representation [31.305851707485967]
  We introduce SphereDiff, a novel approach for seamless 360-degree panoramic image and video generation using state-of-the-art diffusion models without additional tuning.<n>We extend MultiDiffusion to spherical latent space and propose a spherical latent sampling method to enable direct use of pretrained diffusion models.<n>Our method outperforms existing approaches in generating 360-degree panoramic content while maintaining high fidelity, making it a robust solution for immersive AR/VR applications.
  arXiv  Detail & Related papers  (2025-04-19T19:59:11Z)
• Geometric Algebra Planes: Convex Implicit Neural Volumes [70.12234371845445]
  We show that GA-Planes is equivalent to a sparse low-rank factor plus low-resolution matrix. We also show that GA-Planes can be adapted for many existing representations.
  arXiv  Detail & Related papers  (2024-11-20T18:21:58Z)
• GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis [16.733855781461802]
  Implicit deformable representations commonly model motion with a canonical space and time-dependent deformation field.<n>GauFRe, uses a forward-warping deformation to explicitly model non-rigid transformations of scene geometry.<n>Experiments show our method achieves competitive results and higher efficiency than previous state-of-the-art NeRF and Gaussian-based methods.
  arXiv  Detail & Related papers  (2023-12-18T18:59:03Z)
• InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering [55.70938412352287]
  We present an information-theoretic regularization technique for few-shot novel view synthesis based on neural implicit representation. The proposed approach minimizes potential reconstruction inconsistency that happens due to insufficient viewpoints. We achieve consistently improved performance compared to existing neural view synthesis methods by large margins on multiple standard benchmarks.
  arXiv  Detail & Related papers  (2021-12-31T11:56:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.