Multi View Diffuser
Multi-view diffusers are a class of generative models leveraging diffusion processes to create consistent and realistic multi-view images or other multimodal data, such as audio-video pairs or sensor fusion data. Current research focuses on improving the efficiency and robustness of these models, often employing transformer-based architectures and techniques like contrastive learning or incorporating spatial and temporal information for better control and generalization. These advancements are significant for applications ranging from 3D object detection and robotic manipulation to generating synthetic datasets for training other AI models and enhancing medical imaging analysis.
Papers
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