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This AI Paper Introduces a Groundbreaking Method for Modeling 3D Scene Dynamics Using Multi-View Videos

NVFi tackles the intricate challenge of comprehending and predicting the dynamics within 3D scenes evolving over time, a task critical for applications in augmented reality, gaming, and cinematography. While humans effortlessly grasp the physics and geometry of such scenes, existing computational models struggle to explicitly learn these properties from multi-view videos. The core issue lies…

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Visualizing trade flow in Python maps — Part I: Bi-directional trade flow maps | by Himalaya Bir Shrestha | Dec, 2023

In the trade flow maps, I aimed to represent two-way trade relationships between countries. For example, the export from Nepal to India would be represented by the first arrow (A1-A2) and the import by Nepal from India would be represented by a second arrow (A3-A4). In this way, each country pair relationship would require four…

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NTU Researchers Unveil Upscale-A-Video: Pioneering Text-Guided Latent Diffusion for Enhanced Video Super-Resolution

Video super-resolution, aiming to elevate the quality of low-quality videos to high fidelity, faces the daunting challenge of addressing diverse and intricate degradations commonly found in real-world scenarios. Unlike previous focuses on synthetic or specific camera-related degradations, the complexity arises from multiple unknown factors like downsampling, noise, blur, flickering, and video compression. While recent CNN-based…

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Researchers from CMU and Max Planck Institute Unveil WHAM: A Groundbreaking AI Approach for Precise and Efficient 3D Human Motion Estimation from Video

3D human motion reconstruction is a complex process that involves accurately capturing and modeling the movements of a human subject in three dimensions. This job becomes even more challenging when dealing with videos captured by a moving camera in real-world settings, as they often contain issues like foot sliding. However, a team of researchers from…

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