hold

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.

Woodpecker is a tool designed to correct hallucinations in Multimodal Large Language Models (MLLMs) by introducing a training-free method that picks out and corrects inconsistencies between generated text and image content. It consists of five stages: key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction. Woodpecker can be easily integrated with different MLLMs and provides interpretable results by accessing intermediate outputs of the stages. The tool has shown significant improvements in accuracy over baseline models like MiniGPT-4 and mPLUG-Owl.

Nixtla is a production-ready generative pretrained transformer for time series forecasting and anomaly detection. It can accurately predict various domains such as retail, electricity, finance, and IoT with just a few lines of code. TimeGPT introduces a paradigm shift with its standout performance, efficiency, and simplicity, making it accessible even to users with minimal coding experience. The model is based on self-attention and is independently trained on a vast time series dataset to minimize forecasting error. It offers features like zero-shot inference, fine-tuning, API access, adding exogenous variables, multiple series forecasting, custom loss function, cross-validation, prediction intervals, and handling irregular timestamps.

MME-RealWorld is a benchmark designed to address real-world applications with practical relevance, featuring 13,366 high-resolution images and 29,429 annotations across 43 tasks. It aims to provide substantial recognition challenges and overcome common barriers in existing Multimodal Large Language Model benchmarks, such as small data scale, restricted data quality, and insufficient task difficulty. The dataset offers advantages in data scale, data quality, task difficulty, and real-world utility compared to existing benchmarks. It also includes a Chinese version with additional images and QA pairs focused on Chinese scenarios.

RepoAgent is an LLM-powered framework designed for repository-level code documentation generation. It automates the process of detecting changes in Git repositories, analyzing code structure through AST, identifying inter-object relationships, replacing Markdown content, and executing multi-threaded operations. The tool aims to assist developers in understanding and maintaining codebases by providing comprehensive documentation, ultimately improving efficiency and saving time.

LLM Blueprint is an official implementation of a paper that enables text-to-image generation with complex and detailed prompts. It leverages Large Language Models (LLMs) to extract critical components from text prompts, including bounding box coordinates for foreground objects, detailed textual descriptions for individual objects, and a succinct background context. The tool operates in two phases: Global Scene Generation creates an initial scene using object layouts and background context, and an Iterative Refinement Scheme refines box-level content to align with textual descriptions, ensuring consistency and improving recall compared to baseline diffusion models.

CrewAI is a cutting-edge framework designed to orchestrate role-playing autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It enables AI agents to assume roles, share goals, and operate in a cohesive unit, much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions. With features like role-based agent design, autonomous inter-agent delegation, flexible task management, and support for various LLMs, CrewAI offers a dynamic and adaptable solution for both development and production workflows.

Dispider is an implementation enabling real-time interactions with streaming videos, providing continuous feedback in live scenarios. It separates perception, decision-making, and reaction into asynchronous modules, ensuring timely interactions. Dispider outperforms VideoLLM-online on benchmarks like StreamingBench and excels in temporal reasoning. The tool requires CUDA 11.8 and specific library versions for optimal performance.

Macaw-LLM is a pioneering multi-modal language modeling tool that seamlessly integrates image, audio, video, and text data. It builds upon CLIP, Whisper, and LLaMA models to process and analyze multi-modal information effectively. The tool boasts features like simple and fast alignment, one-stage instruction fine-tuning, and a new multi-modal instruction dataset. It enables users to align multi-modal features efficiently, encode instructions, and generate responses across different data types.

SheetCopilot is an assistant agent that manipulates spreadsheets by following user commands. It leverages Large Language Models (LLMs) to interact with spreadsheets like a human expert, enabling non-expert users to complete tasks on complex software such as Google Sheets and Excel via a language interface. The tool observes spreadsheet states, polishes generated solutions based on external action documents and error feedback, and aims to improve success rate and efficiency. SheetCopilot offers a dataset with diverse task categories and operations, supporting operations like entry & manipulation, management, formatting, charts, and pivot tables. Users can interact with SheetCopilot in Excel or Google Sheets, executing tasks like calculating revenue, creating pivot tables, and plotting charts. The tool's evaluation includes performance comparisons with leading LLMs and VBA-based methods on specific datasets, showcasing its capabilities in controlling various aspects of a spreadsheet.

This repository contains a sophisticated deterministic graph-based solution for answering complex questions using a controllable autonomous agent. The solution is designed to ensure that answers are solely based on the provided data, avoiding hallucinations. It involves various steps such as PDF loading, text preprocessing, summarization, database creation, encoding, and utilizing large language models. The algorithm follows a detailed workflow involving planning, retrieval, answering, replanning, content distillation, and performance evaluation. Heuristics and techniques implemented focus on content encoding, anonymizing questions, task breakdown, content distillation, chain of thought answering, verification, and model performance evaluation.

POML (Prompt Orchestration Markup Language) is a novel markup language designed to bring structure, maintainability, and versatility to advanced prompt engineering for Large Language Models (LLMs). It addresses common challenges in prompt development, such as lack of structure, complex data integration, format sensitivity, and inadequate tooling. POML provides a systematic way to organize prompt components, integrate diverse data types seamlessly, and manage presentation variations, empowering developers to create more sophisticated and reliable LLM applications.

MicroLens is a content-driven micro-video recommendation dataset at scale. It provides a large dataset with multimodal data, including raw text, images, audio, video, and video comments, for tasks such as multi-modal recommendation, foundation model building, and fairness recommendation. The dataset is available in two versions: MicroLens-50K and MicroLens-100K, with extracted features for multimodal recommendation tasks. Researchers can access the dataset through provided links and reach out to the corresponding author for the complete dataset. The repository also includes codes for various algorithms like VideoRec, IDRec, and VIDRec, each implementing different video models and baselines.

Docs2KG is a tool designed for constructing a unified knowledge graph from heterogeneous documents. It addresses the challenges of digitizing diverse unstructured documents and constructing a high-quality knowledge graph with less effort. The tool combines bottom-up and top-down approaches, utilizing a human-LLM collaborative interface to enhance the generated knowledge graph. It organizes the knowledge graph into MetaKG, LayoutKG, and SemanticKG, providing a comprehensive view of document content. Docs2KG aims to streamline the process of knowledge graph construction and offers metrics for evaluating the quality of automatic construction.

FuseMedML is a Python framework designed to accelerate machine learning-based discovery in the medical field by promoting code reuse. It provides a flexible design concept where data is stored in a nested dictionary, allowing easy handling of multi-modality information. The framework includes components for creating custom models, loss functions, metrics, and data processing operators. Additionally, FuseMedML offers 'batteries included' key components such as fuse.data for data processing, fuse.eval for model evaluation, and fuse.dl for reusable deep learning components. It supports PyTorch and PyTorch Lightning libraries and encourages the creation of domain extensions for specific medical domains.

ChemBench is a project aimed at expanding chemistry benchmark tasks in a BIG-bench compatible way, providing a pipeline to benchmark frontier and open models. It allows users to run benchmarking tasks on models with existing presets, offering predefined parameters and processing steps. The library facilitates benchmarking models on the entire suite, addressing challenges such as prompt structure, parsing, and scoring methods. Users can contribute to the project by following the developer notes.

MathCoder is a repository focused on enhancing mathematical reasoning by fine-tuning open-source language models to use code for modeling and deriving math equations. It introduces MathCodeInstruct dataset with solutions interleaving natural language, code, and execution results. The repository provides MathCoder models capable of generating code-based solutions for challenging math problems, achieving state-of-the-art scores on MATH and GSM8K datasets. It offers tools for model deployment, inference, and evaluation, along with a citation for referencing the work.

AlphaFold3 is an implementation of the Alpha Fold 3 model in PyTorch for accurate structure prediction of biomolecular interactions. It includes modules for genetic diffusion and full model examples for forward pass computations. The tool allows users to generate random pair and single representations, operate on atomic coordinates, and perform structure predictions based on input tensors. The implementation also provides functionalities for training and evaluating the model.

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.

AliceVision is a photogrammetric computer vision framework which provides a 3D reconstruction pipeline. It is designed to process images from different viewpoints and create detailed 3D models of objects or scenes. The framework includes various algorithms for feature detection, matching, and structure from motion. AliceVision is suitable for researchers, developers, and enthusiasts interested in computer vision, photogrammetry, and 3D modeling. It can be used for applications such as creating 3D models of buildings, archaeological sites, or objects for virtual reality and augmented reality experiences.

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.

The stable-diffusion.cpp repository provides an implementation for inferring stable diffusion in pure C/C++. It offers features such as support for different versions of stable diffusion, lightweight and dependency-free implementation, various quantization support, memory-efficient CPU inference, GPU acceleration, and more. Users can download the built executable program or build it manually. The repository also includes instructions for downloading weights, building from scratch, using different acceleration methods, running the tool, converting weights, and utilizing various features like Flash Attention, ESRGAN upscaling, PhotoMaker support, and more. Additionally, it mentions future TODOs and provides information on memory requirements, bindings, UIs, contributors, and references.

JoliGEN is an integrated framework for training custom generative AI image-to-image models. It implements GAN, Diffusion, and Consistency models for various image translation tasks, including domain and style adaptation with conservation of semantics. The tool is designed for real-world applications such as Controlled Image Generation, Augmented Reality, Dataset Smart Augmentation, and Synthetic to Real transforms. JoliGEN allows for fast and stable training with a REST API server for simplified deployment. It offers a wide range of options and parameters with detailed documentation available for models, dataset formats, and data augmentation.

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.

GPUPixel is a real-time, high-performance image and video filter library written in C++11 and based on OpenGL/ES. It incorporates a built-in beauty face filter that achieves commercial-grade beauty effects. The library is extremely easy to compile and integrate with a small size, supporting platforms including iOS, Android, Mac, Windows, and Linux. GPUPixel provides various filters like skin smoothing, whitening, face slimming, big eyes, lipstick, and blush. It supports input formats like YUV420P, RGBA, JPEG, PNG, and output formats like RGBA and YUV420P. The library's performance on devices like iPhone and Android is optimized, with low CPU usage and fast processing times. GPUPixel's lib size is compact, making it suitable for mobile and desktop applications.

MediaPipe-rs is a Rust library designed for MediaPipe tasks on WasmEdge WASI-NN. It offers easy-to-use low-code APIs similar to mediapipe-python, with low overhead and flexibility for custom media input. The library supports various tasks like object detection, image classification, gesture recognition, and more, including TfLite models, TF Hub models, and custom models. Users can create task instances, run sessions for pre-processing, inference, and post-processing, and speed up processing by reusing sessions. The library also provides support for audio tasks using audio data from symphonia, ffmpeg, or raw audio. Users can choose between CPU, GPU, or TPU devices for processing.

LL3DA is a Large Language 3D Assistant that responds to both visual and textual interactions within complex 3D environments. It aims to help Large Multimodal Models (LMM) comprehend, reason, and plan in diverse 3D scenes by directly taking point cloud input and responding to textual instructions and visual prompts. LL3DA achieves remarkable results in 3D Dense Captioning and 3D Question Answering, surpassing various 3D vision-language models. The code is fully released, allowing users to train customized models and work with pre-trained weights. The tool supports training with different LLM backends and provides scripts for tuning and evaluating models on various tasks.

Generative Models by Stability AI is a repository that provides various generative models for research purposes. It includes models like Stable Video 4D (SV4D) for video synthesis, Stable Video 3D (SV3D) for multi-view synthesis, SDXL-Turbo for text-to-image generation, and more. The repository focuses on modularity and implements a config-driven approach for building and combining submodules. It supports training with PyTorch Lightning and offers inference demos for different models. Users can access pre-trained models like SDXL-base-1.0 and SDXL-refiner-1.0 under a CreativeML Open RAIL++-M license. The codebase also includes tools for invisible watermark detection in generated images.