Best AI tools for< Reconstruct Objects >

Remover.app is a free online tool that allows you to remove unwanted objects, people, or defects from your photos. It uses artificial intelligence to reconstruct the background behind the object, so you can achieve professional results in just a few clicks. Remover.app is easy to use, simply upload your photo and brush over the object you want to remove. The AI will do the rest! Remover.app is perfect for removing unwanted objects from product photos, real estate photos, and even family photos. It's also great for removing blemishes, wrinkles, and other imperfections from your photos.

Mind-Video is an AI tool that focuses on high-quality video reconstruction from brain activity data obtained through fMRI scans. The tool aims to bridge the gap between image and video brain decoding by leveraging masked brain modeling, multimodal contrastive learning, spatiotemporal attention, and co-training with an augmented Stable Diffusion model. It is designed to enhance the generation consistency and accuracy of reconstructing continuous visual experiences from brain activities, ultimately contributing to a deeper understanding of human cognitive processes.

CEBRA is a machine-learning method that compresses time series data to reveal hidden structures in the variability of the data. It excels in analyzing behavioral and neural data simultaneously, allowing for the decoding of activity from the visual cortex of the mouse brain to reconstruct viewed videos. CEBRA is a novel encoding method that leverages both behavioral and neural data to produce consistent and high-performance latent spaces, enabling the mapping of space, uncovering complex kinematic features, and providing rapid, high-accuracy decoding of natural movies from the visual cortex.

Historica is an AI-powered history map application that offers a unique experience of exploring civilization's timeline through meticulously reconstructed landscapes. The application leverages generative AI technology to craft historical maps, providing users with a rich tapestry of history. Historica aims to revive the past and immerse users in the artistry of historical storytelling.

This repository contains the code for HOLD, a method that jointly reconstructs hands and objects from monocular videos without assuming a pre-scanned object template. It can reconstruct 3D geometries of novel objects and hands, enabling template-free bimanual hand-object reconstruction, textureless object interaction with hands, and multiple objects interaction with hands. The repository provides instructions to download in-the-wild videos from HOLD, preprocess and train on custom videos, a volumetric rendering framework, a generalized codebase for single and two hand interaction with objects, a viewer to interact with predictions, and code to evaluate and compare with HOLD in HO3D. The repository also includes documentation for setup, training, evaluation, visualization, preprocessing custom sequences, and using HOLD on ARCTIC.

ShapeLLM is the first 3D Multimodal Large Language Model designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages. It supports single-view colored point cloud input and introduces a robust 3D QA benchmark, 3D MM-Vet, encompassing various variants. The model extends the powerful point encoder architecture, ReCon++, achieving state-of-the-art performance across a range of representation learning tasks. ShapeLLM can be used for tasks such as training, zero-shot understanding, visual grounding, few-shot learning, and zero-shot learning on 3D MM-Vet.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

Mountain GOAP is a generic C# GOAP (Goal Oriented Action Planning) library for creating AI agents in games. It favors composition over inheritance, supports multiple weighted goals, and uses A* pathfinding to plan paths through sequential actions. The library includes concepts like agents, goals, actions, sensors, permutation selectors, cost callbacks, state mutators, state checkers, and a logger. It also features event handling for agent planning and execution. The project structure includes examples, API documentation, and internal classes for planning and execution.

This repository is a refactored and updated version of `stable-audio-tools`, an open-source code for audio/music generative models originally by Stability AI. It contains refactored codes for improved readability and usability, useful scripts for evaluating and playing with trained models, and instructions on how to train models such as `Stable Audio 2.0`. The repository does not contain any pretrained checkpoints. Requirements include PyTorch 2.0 or later for Flash Attention support and Python 3.8.10 or later for development. The repository provides guidance on installing, building a training environment using Docker or Singularity, logging with Weights & Biases, training configurations, and stages for VAE-GAN and Diffusion Transformer (DiT) training.

ModelFusion is an abstraction layer for integrating AI models into JavaScript and TypeScript applications, unifying the API for common operations such as text streaming, object generation, and tool usage. It provides features to support production environments, including observability hooks, logging, and automatic retries. You can use ModelFusion to build AI applications, chatbots, and agents. ModelFusion is a non-commercial open source project that is community-driven. You can use it with any supported provider. ModelFusion supports a wide range of models including text generation, image generation, vision, text-to-speech, speech-to-text, and embedding models. ModelFusion infers TypeScript types wherever possible and validates model responses. ModelFusion provides an observer framework and logging support. ModelFusion ensures seamless operation through automatic retries, throttling, and error handling mechanisms. ModelFusion is fully tree-shakeable, can be used in serverless environments, and only uses a minimal set of dependencies.

The 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

AICSImageIO is a Python tool for Image Reading, Metadata Conversion, and Image Writing for Microscopy Images. It supports various file formats like OME-TIFF, TIFF, ND2, DV, CZI, LIF, PNG, GIF, and Bio-Formats. Users can read and write metadata and imaging data, work with different file systems like local paths, HTTP URLs, s3fs, and gcsfs. The tool provides functionalities for full image reading, delayed image reading, mosaic image reading, metadata reading, xarray coordinate plane attachment, cloud IO support, and saving to OME-TIFF. It also offers benchmarking and developer resources.

This repository contains a demo application for DepthAI, a tool that can load different networks, create pipelines, record video, and more. It provides documentation for installation and usage, including running programs through Docker. Users can explore DepthAI features via command line arguments or a clickable QT interface. Supported models include various AI models for tasks like face detection, human pose estimation, and object detection. The tool collects anonymous usage statistics by default, which can be disabled. Users can report issues to the development team for support and troubleshooting.

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.

AI Audio Datasets List (AI-ADL) is a comprehensive collection of datasets consisting of speech, music, and sound effects, used for Generative AI, AIGC, AI model training, and audio applications. It includes datasets for speech recognition, speech synthesis, music information retrieval, music generation, audio processing, sound synthesis, and more. The repository provides a curated list of diverse datasets suitable for various AI audio tasks.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

AIMET is a library that provides advanced model quantization and compression techniques for trained neural network models. It provides features that have been proven to improve run-time performance of deep learning neural network models with lower compute and memory requirements and minimal impact to task accuracy. AIMET is designed to work with PyTorch, TensorFlow and ONNX models. We also host the AIMET Model Zoo - a collection of popular neural network models optimized for 8-bit inference. We also provide recipes for users to quantize floating point models using AIMET.

Concise NeRF guide, summarizing top papers and aiding research.

Tell me your story and I'll reconstruct it.

The first-ever Multifaceted AI Chat Bot designed to deconstruct bias across all sectors.

🗣 Fix, Write or Deconstruct Speeches