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This newsletter will discuss some cool AI research papers, AI tools, and AI Startups. Happy learning!

➡️ 3D Body Models Now Have Sound: Meta AI Introduces an Artificial Intelligence Model that can Generate Accurate 3D Spatial Audio for Full Human Bodies

How can we model 3D spatial audio corresponding to human body movements and speech? This paper introduces a novel model to bridge this gap in computer vision. It generates accurate 3D spatial audio for full human bodies, using audio signals from headset microphones and body pose data as inputs. The output is a 3D sound field around the person's body, enabling spatial audio rendering from any point in 3D space. To support this, a unique multimodal dataset was created, featuring recordings from multiple cameras and a 345-microphone array. Empirical evaluations show the model's effectiveness in producing precise body-induced sound fields, with the dataset and code made publicly available.

➡️  Meet DeepMind’s GraphCast: A Leap Forward in Machine Learning-Powered Weather Forecasting

How can machine learning improve global medium-range weather forecasting, traditionally reliant on compute-intensive numerical methods? "GraphCast" offers a novel approach, utilizing machine learning trained on historical weather data. This method can forecast hundreds of variables across a 10-day span, at a 0.25° global resolution and, remarkably, in under a minute. Outclassing the most accurate operational deterministic systems in 90% of 1380 verification targets, GraphCast excels in predicting severe weather events like tropical cyclones, atmospheric rivers, and extreme temperatures. Representing a significant advancement in both accuracy and efficiency, GraphCast demonstrates the potential of machine learning in modeling complex dynamical systems.

➡️  This AI Research from Adobe Proposes a Large Reconstruction Model (LRM) that Predicts the 3D Model of an Object from a Single Input Image within 5 Seconds

How can AI predict 3D models from single images rapidly and accurately? This paper introduces the first Large Reconstruction Model (LRM), a transformative approach in 3D object reconstruction. Unlike previous methods reliant on small, category-specific datasets, LRM leverages a transformer-based architecture with 500 million parameters to directly predict a neural radiance field (NeRF) from an input image. Trained end-to-end on an expansive dataset of approximately 1 million objects, including synthetic and real captures from Objaverse and MVImgNet, LRM demonstrates remarkable generalizability. This capacity enables it to produce high-quality 3D reconstructions from diverse inputs, including real-world images and those generated by AI models, all within a swift 5-second timeframe.

➡️ Meet LocoMuJoCo: A Novel Machine Learning Benchmark Designed to Facilitate Rigorous Evaluation and Comparison of Imitation Learning Algorithms

How can Imitation Learning (IL) be effectively applied for agile locomotion in more complex, real-world scenarios, beyond simplified toy tasks? To address this, a new research introduces a comprehensive benchmark tailored for evaluating IL algorithms in locomotion. This benchmark features a diverse array of environments, including models for quadrupeds, bipeds, and musculoskeletal humans. It comes equipped with extensive datasets encompassing real noisy motion capture data, expert data, and sub-optimal data, facilitating tests across varying difficulty levels. The benchmark also supports dynamics randomization and presents a range of partially observable tasks. Additionally, it includes handcrafted metrics for each task and is bundled with state-of-the-art baseline algorithms, streamlining evaluation and benchmarking processes.

✅ Featured AI Tools For You

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