Abstract
In the current era of generative AI breakthroughs, generating panoramic scenes from a single input image remains a key challenge. Most existing methods use diffusion-based iterative or simultaneous multi-view inpainting. However, the lack of global scene layout priors leads to subpar outputs with duplicated objects (e.g., multiple beds in a bedroom) or requires time-consuming human text inputs for each view. We propose L-MAGIC, a novel method leveraging large language models for guidance while diffusing multiple coherent views of 360 degree panoramic scenes. L-MAGIC harnesses pre-trained diffusion and language models without fine-tuning, ensuring zero-shot performance. The output quality is further enhanced by super-resolution and multi-view fusion techniques. Extensive experiments demonstrate that the resulting panoramic scenes feature better scene layouts and perspective view rendering quality compared to related works, with >70% preference in human evaluations. Combined with conditional diffusion models, L-MAGIC can accept various input modalities, including but not limited to text, depth maps, sketches, and colored scripts. Applying depth estimation further enables 3D point cloud generation and dynamic scene exploration with fluid camera motion. Code is available at this https URL. The video presentation is available at this https URL.
Abstract (translated)
在当前的生成型 AI 突破时代，从单个输入图像生成全景场景仍然是一个关键挑战。大多数现有方法使用扩散为基础的迭代或同时多视角修复。然而，全局场景布局先验的缺乏导致具有重复对象的低质量输出（例如，卧室中的多个床）或需要花费时间的人类文本输入每个视角。我们提出了 L-MAGIC，一种利用大型语言模型指导的多视角360度全景场景扩散的新方法。L-MAGIC 利用预训练的扩散和语言模型，无需微调，实现零散性能。通过超分辨率和高维融合技术进一步提高了输出质量。大量实验证明，生成的全景场景具有更好的布局和视图渲染质量，与相关作品相比，偏好率超过70%。结合条件扩散模型，L-MAGIC 可以接受各种输入模式，包括但不仅限于文本、深度图、草图和彩色脚本。应用深度估计还进一步实现了随机的相机运动下3D点云生成和动态场景探索。代码可以从该链接获取。视频演示可以从该链接获取。
URL
https://arxiv.org/abs/2406.01843
