全景图生成：AI终于能理解球面几何了
以前AI生成全景图，经常把地平线搞弯、柱子变歪，因为它把球面当平面处理。这篇论文让AI先学“几何感知”——在训练时额外生成深度图、用圆形填充保持左右无缝衔接、加一个损失函数惩罚几何不一致。结果全景图更真实，尤其在衡量全景畸变的指标上提升明显。它不是你明天就能用的工具，但说明AI开始理解“球面”和“平面”的区别了。
📄 原文摘要(英文)
In this work, we present Canvas360, a two-stage framework for in-context panoramic generation that combines geometry-aware pretraining with downstream task-specific fine-tuning. To address the lack of large-scale, high-quality training data tailored to in-context panoramic tasks, we propose Canvas360Dataset, a collection of 1M high-quality paired panoramic samples for style transfer, inpainting, outpainting, and editing, enabling effective supervision across diverse in-context generation scenarios. On the modeling side, Canvas360 enhances text-to-panorama generation through parallel depth generation, velocity circular padding, and similarity loss regularization, enabling the model to learn geometry-aware representations, capture object distortion details, and improve geometric consistency and global coherence. Furthermore, empowered by strong panoramic priors, Canvas360 enables a unified in-context panoramic generation framework that supports diverse downstream tasks via token-level concatenation, surpassing prior methods in both task coverage and modeling flexibility. Extensive experiments show that Canvas360 improves panoramic image fidelity, achieving particularly strong performance on the panorama-specific FAED metric and competitive or leading results across the reported quantitative evaluations. More information can be found on our project page: https://zry000.github.io/Canvas360/