arXiv:2602.06122v1 Announce Type: new Abstract: Creating high-fidelity, animatable 3D talking heads is crucial for immersive applications, yet often hindered by the prevalence of low-quality image or video sources, which yield poor 3D reconstructions. In this paper, we introduce SuperHead, a novel framework for enhancing low-resolution, animatable 3D head avatars. The core challenge lies in synthesizing high-quality geometry and textures, while ensuring both 3D and temporal consistency during animation and preserving subject identity. Despite recent progress in image, video […]

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As companies grow, they can expand their offerings, reach new customers and markets, enhance profitability, and position themselves ahead of competitors. Whether trying to expand their company size by adding new departments or increase revenue by improving productivity per employee, companies can achieve growth through various strategies. One of the strongest company growth strategies is retaining skilled employees who drive productivity, innovation, morale, and customer satisfaction. Employee retention is a company’s ability to maintain a stable workforce by […]

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Release: luau-wasm 0.1a0 See Publishing WASM wheels to PyPI for use with Pyodide for details. Tags: lua, webassembly, pyodide

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Hello, I wanted to ask how do you restrict the output vocabulary/ possible actions of VLAs. Specifically I am reading currently the research papers of RT-2 and OpenVLA. OpenVLA references RT-2 and RT-2 says nothing specifically, it just says in the fine-tuning phase: “Thus, to ensure that RT-2 outputs valid action tokens during decoding, we constrain its output vocabulary via only sampling valid action tokens when the model is prompted with a robot-action task …” So do you […]

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Federated Rank Learning (FRL) is a promising Federated Learning (FL) paradigm designed to be resilient against model poisoning attacks due to its discrete, ranking-based update mechanism. Unlike traditional FL methods that rely on model updates, FRL leverages discrete rankings as a communication parameter between clients and the server. This approach significantly reduces communication costs and limits an adversary’s ability to scale or optimize malicious updates in the continuous space, thereby enhancing its robustness. This makes FRL particularly appealing […]

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Harmful algal blooms (HABs) can threaten coastal infrastructure, fisheries, and desalination dependent water supplies. This project (REDNET-ML) develops a reproducible machine learning pipeline for HAB risk detection along the Omani coastline using multi sensor satellite data and non leaky evaluation. The system fuses (i) Sentinel-2 optical chips (high spatial resolution) processed into spectral indices and texture signals, (ii) MODIS Level-3 ocean color and thermal indicators, and (iii) learned image evidence from object detectors trained to highlight bloom like […]

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Tropical cyclones are dangerous natural hazards, but their hazard is challenging to quantify directly from historical datasets due to limited dataset size and quality. Models of cyclone intensification fill this data gap by simulating huge ensembles of synthetic hurricanes based on estimates of the storm’s large scale environment. Both physics-based and statistical/ML intensification models have been developed to tackle this problem, but an open question is: can a physically reasonable and simple physics-style differential equation model of intensification […]

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