A 360-degree (omni-directional) image provides an all-encompassing spherical
view of a scene. Recently, there has been an increasing interest in
synthesising 360-degree images from conventional narrow field of view (NFoV)
images captured by digital cameras and smartphones, for providing immersive
experiences in various scenarios such as virtual reality. Yet, existing methods
typically fall short in synthesizing intricate visual details or ensure the
generated images align consistently with user-provided prompts. In this study,
autoregressive omni-aware generative network (AOG-Net) is proposed for
360-degree image generation by out-painting an incomplete 360-degree image
progressively with NFoV and text guidances joinly or individually. This
autoregressive scheme not only allows for deriving finer-grained and
text-consistent patterns by dynamically generating and adjusting the process
but also offers users greater flexibility to edit their conditions throughout
the generation process. A global-local conditioning mechanism is devised to
comprehensively formulate the outpainting guidance in each autoregressive step.
Text guidances, omni-visual cues, NFoV inputs and omni-geometry are encoded and
further formulated with cross-attention based transformers into a global stream
and a local stream into a conditioned generative backbone model. As AOG-Net is
compatible to leverage large-scale models for the conditional encoder and the
generative prior, it enables the generation to use extensive open-vocabulary
text guidances. Comprehensive experiments on two commonly used 360-degree image
datasets for both indoor and outdoor settings demonstrate the state-of-the-art
performance of our proposed method. Our code will be made publicly available.

360 度图像合成的研究中提出了自回归全方位感知生成网络（AOG-Net），通过在不完整的 360 度图像中使用不完全视野和文本指导逐步完成生成，以获取细粒度且与文本一致的模式，实现了更大的编辑灵活性。实验表明，该方法在室内和室外环境下的两个常用数据集上取得了最先进的性能。