Best AI tools for< Geometric Modeling >

Vectorizer.AI is an online tool that allows users to convert PNG and JPG images to SVG vectors quickly and easily using artificial intelligence. The tool analyzes, processes, and converts images from pixels to geometric shapes fully automatically. It offers a full-featured deep vector engine, proprietary computational geometry framework, and various shape fitting options to produce high-quality vector images. Vectorizer.AI supports multiple output formats, including SVG, PDF, EPS, DXF, and PNG, and provides features like symmetry modeling, adaptive simplification, palette control, and sub-pixel precision.

Tattoo AI Design is a free and powerful AI tattoo generator and design tool that allows users to unleash their personal tattoo ideas and create unique tattoo designs with the help of artificial intelligence technology. Users can input their tattoo descriptions and let the AI generate tattoo ideas within seconds, enabling them to explore various tattoo styles and motifs. The platform supports traditional tattoos, neo-traditional tattoos, watercolor tattoos, geometric tattoos, and more, offering a wide range of design possibilities for tattoo enthusiasts.

SwitchLight Studio is an AI-powered lighting tool designed for filmmakers, offering advanced features such as AI Virtual Production, individual AI features like background removal and PBR material extraction, Physically Based Rendering, PBR Neural Enhancer, and Light Map Extraction. It revolutionizes post-production by allowing users to change lighting effects, simulate real-world lighting, and enhance realism in rendering. The tool supports 4K+ resolution videos, guarantees temporal consistency, and provides enterprise features like Nuke Plugin, Command Line Interface, and Multi-OS support. With a focus on privacy, SwitchLight ensures local processing of image and video data without cloud uploads.

Math Sniper is an AI-powered application designed to provide precise math solutions, exam preparation assistance, and exploration of mathematical concepts. The app offers step-by-step solutions to math challenges at all levels, connects users with math tutors for personalized help, and covers a wide range of subjects beyond mathematics, such as biology, chemistry, physics, history, economics, and language tasks. With features like Snap & Ask for instant answers, step-by-step explanations, and a user-friendly interface, Math Sniper aims to enhance users' understanding of complex concepts and facilitate learning in various disciplines.

Math.now is a free online math AI solver powered by Math GPT, offering instant, step-by-step solutions for a wide range of mathematical problems. Users can input math problems or upload photos for analysis, interact with the math AI bot for explanations, and receive real-time assistance. The application supports algebra, geometry, calculus, and word problems, providing detailed guidance and personalized learning experiences. Math.now's AI solver ensures accuracy, efficiency, and accessibility for students, educators, and self-learners.

Rodin is a free AI 3D model generator that allows users to create high-quality 3D assets from images and text. Users can upload photos from any angle and generate assets using a large-scale generative model. The tool offers features like multi-view fusion, geometry and material preview, and subscription options for enhanced functionality. Rodin is designed to simplify the process of creating 3D models for various purposes such as business, education, and enterprise.

Solvely is an all-in-one AI homework helper that provides step-by-step solutions for all courses, from K12 to Graduate school. It covers a wide range of subjects, from STEM to Liberal Arts, and offers detailed explanations to help users understand complex problems. With a high accuracy rate and a user-friendly interface, Solvely aims to make learning and problem-solving easier for students, parents, and teachers worldwide.

AI Math Solver is an advanced AI application that leverages multi-modal AI technology to assist users in solving math problems step by step. Users can upload photos or describe math problems to receive accurate solutions efficiently. The application also supports Latex for displaying math formulas, allows users to save and share solved math problems, and offers solutions for set operations, equations, and geometry problems. AI Math Solver is designed to outperform human performance in math challenges, making it a powerful tool for students and professionals alike.

AI Math is an online math solver that uses artificial intelligence to help users solve math problems. It can solve a wide range of math problems, including arithmetic, algebra, geometry, trigonometry, calculus, combinations, word problems, statistics, and probability. AI Math is available in over 30 languages and is free to use. It is a valuable tool for students, educators, and anyone who needs help with math.

Kaedim is an AI-powered art outsourcing platform that offers a ready-to-scale, on-demand solution for 3D content creation. It combines machine learning algorithms and an expert 3D team to deliver production-quality assets in minutes, empowering game developers to create stunning graphics and ship 10x faster. Kaedim's platform streamlines the art production process, providing real-time overviews, frictionless iterations, and personalized support to enhance workflow efficiency and quality standards. The platform is suitable for game development teams of all sizes, from indie to AAA studios, and offers end-to-end pipeline services from high poly geometry to production-ready 3D assets.

The Visual Computing & Artificial Intelligence Lab at TUM is a group of research enthusiasts advancing cutting-edge research at the intersection of computer vision, computer graphics, and artificial intelligence. Our research mission is to obtain highly-realistic digital replica of the real world, which include representations of detailed 3D geometries, surface textures, and material definitions of both static and dynamic scene environments. In our research, we heavily build on advances in modern machine learning, and develop novel methods that enable us to learn strong priors to fuel 3D reconstruction techniques. Ultimately, we aim to obtain holographic representations that are visually indistinguishable from the real world, ideally captured from a simple webcam or mobile phone. We believe this is a critical component in facilitating immersive augmented and virtual reality applications, and will have a substantial positive impact in modern digital societies.

PINNACLE is a flexible geometric deep learning approach that trains on contextualized protein interaction networks to generate context-aware protein representations. It provides protein representations split across various cell-type contexts from different tissues and organs. The tool can be fine-tuned to study the genomic effects of drugs and nominate promising protein targets and cell-type contexts for further investigation. PINNACLE exemplifies the paradigm of incorporating context-specific effects for studying biological systems, especially the impact of disease and therapeutics.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

The 'ai4math-papers' repository contains a collection of research papers related to AI applications in mathematics, including automated theorem proving, synthetic theorem generation, autoformalization, proof refactoring, premise selection, benchmarks, human-in-the-loop interactions, and constructing examples/counterexamples. The papers cover various topics such as neural theorem proving, reinforcement learning for theorem proving, generative language modeling, formal mathematics statement curriculum learning, and more. The repository serves as a valuable resource for researchers and practitioners interested in the intersection of AI and mathematics.

LLM-Synthetic-Data is a repository focused on real-time, fine-grained LLM-Synthetic-Data generation. It includes methods, surveys, and application areas related to synthetic data for language models. The repository covers topics like pre-training, instruction tuning, model collapse, LLM benchmarking, evaluation, and distillation. It also explores application areas such as mathematical reasoning, code generation, text-to-SQL, alignment, reward modeling, long context, weak-to-strong generalization, agent and tool use, vision and language, factuality, federated learning, generative design, and safety.

The Open MatSci ML Toolkit is a flexible framework for machine learning in materials science. It provides a unified interface to a variety of materials science datasets, as well as a set of tools for data preprocessing, model training, and evaluation. The toolkit is designed to be easy to use for both beginners and experienced researchers, and it can be used to train models for a wide range of tasks, including property prediction, materials discovery, and materials design.

Awesome-AIGC-3D is a curated list of awesome AIGC 3D papers, inspired by awesome-NeRF. It aims to provide a comprehensive overview of the state-of-the-art in AIGC 3D, including papers on text-to-3D generation, 3D scene generation, human avatar generation, and dynamic 3D generation. The repository also includes a list of benchmarks and datasets, talks, companies, and implementations related to AIGC 3D. The description is less than 400 words and provides a concise overview of the repository's content and purpose.

KG-LLM-Papers is a repository that collects papers integrating knowledge graphs (KGs) and large language models (LLMs). It serves as a comprehensive resource for research on the role of KGs in the era of LLMs, covering surveys, methods, and resources related to this integration.

Awesome AI is a curated list of artificial intelligence resources including courses, tools, apps, and open-source projects. It covers a wide range of topics such as machine learning, deep learning, natural language processing, robotics, conversational interfaces, data science, and more. The repository serves as a comprehensive guide for individuals interested in exploring the field of artificial intelligence and its applications across various domains.

Here we will keep track of the AI Game Development Tools, including LLM, Agent, Code, Writer, Image, Texture, Shader, 3D Model, Animation, Video, Audio, Music, Singing Voice and Analytics. 🔥 * Tool (AI LLM) * Game (Agent) * Code * Framework * Writer * Image * Texture * Shader * 3D Model * Avatar * Animation * Video * Audio * Music * Singing Voice * Speech * Analytics * Video Tool

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

Recent LLM (Large Language Models)-based CV and multi-modal works.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

Expert in computer vision, deep learning, ready to assist you with 3d and geometric computer vision. https://github.com/kornia/pixie

RhinoCommonについて教えてくれる

Structured shapes dance with untamed creatures, painting a canvas of awe and wonder.

Direct answers on Lie groups for the knowledgeable.

Your go-to guide for mastering geometry with ease and fun!

A friendly geometry tutor for middle school students, providing answers and short tutorials.

Expert in 3D geometry, physics, engineering, and more.

Expert in QGIS Geometry Generator expressions, providing detailed, educational, and clear guides.

I help parents help their kids with math. Need a 9pm refresher on geometry proofs? I’m here for you.

Euclid's Elements expert

Expert in GH-Python scripting & Grasshopper components

A guide to electron configurations with a fun, cartoony approach.

William Paul Thurston (October 30, 1946 – August 21, 2012) was an American mathematician. He was a pioneer in the field of low-dimensional topology and was awarded the Fields Medal in 1982 for his contributions to the study of 3-manifolds.

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