L-MAGIC: Language Model Assisted Generation of Images with Coherence

Zhipeng Cai, Matthias Mueller, Reiner Birkl, Diana Wofk, Shao-Yen Tseng, Junda Cheng, Gabriela Ben-Melech Stan, Vasudev Lai, Michael Paulitsch; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 7049-7058

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.