Body Scan by Zing
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Zing's Body Scan Report is a fitness tool powered by AI that analyzes a full-body selfie to determine body fat and lean mass percentages. It creates a customized fitness regimen and offers macronutrient plans for fats, proteins, and carbohydrates. The precision of the Body Scan by Zing is on par with costly DEXA scans, with the convenience and privacy of conducting the assessment at home. Additionally, it offers users personalized video workouts and the capability to monitor and assess their progress over time.
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Postshot, created by Jawset, is an AI-driven 3D scanning software that converts video clips into intricate 3D models through advanced methods such as Neural Radiance Fields and Gaussian Splatting. Users just need to record a video of an object with a smartphone, load it into the Postshot desktop application, and the program processes it into a detailed 3D model. This tool is especially attractive to designers, engineers, researchers, and 3D modeling enthusiasts for its user-friendly interface, advanced AI technology, and versatility for various purposes. Featuring an innovative approach and free beta access, Postshot provides an accessible, state-of-the-art solution for creating high-quality 3D models without the need for specialized equipment.
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The Qlone app transforms photos taken on iPhones or iPads into enhanced 3D models for augmented reality by using the Object Capture API on macOS Monterey. It employs photogrammetry and enables scanning through the Qlone app or the processing of images from folders or ZIP archives.
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UBIAI's robust annotation platform offers a quick and effective solution for data labeling, training, and deploying custom NLP models. It includes an OCR Annotation tool for high-quality data labeling, team collaboration, model-assisted labeling, document classification, named entity extraction, multi-lingual annotation, and an OCR annotation tool. UBIAI allows users to streamline their NLP development process by reducing annotation time by 50-80%, lowering costs for testing and validating custom NLP models by 5 times, and decreasing the need for manual annotations by 12 times. It smoothly manages annotation complexities by unlocking data from scanned images and PDF documents. Moreover, UBIAI's auto-labeling function accelerates NLP projects, while its pre-annotation feature helps users kickstart their annotation projects efficiently. Additionally, UBIAI enables users to create training data and export models in various formats, serving as an all-in-one solution for NLP requirements.