Paper Reading AI Learner

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

2024-06-03 23:28:57
Zhipeng Cai, Matthias Mueller, Reiner Birkl, Diana Wofk, Shao-Yen Tseng, JunDa Cheng, Gabriela Ben-Melech Stan, Vasudev Lal, Michael Paulitsch


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。



3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot