Popular GitHub Repositories for Human Factors

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.

Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System

Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

OpenMMLab Pose Estimation Toolbox and Benchmark.

A research prototype of a human-centered web agent

Real time interactive streaming digital human

StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models

[ICLR 2025] Agent S: an open agentic framework that uses computers like a human

Robust recipes to align language models with human and AI preferences

Code repo for realtime multi-person pose estimation in CVPR'17 (Oral)

TTS Towards Human-Sounding Speech

PraisonAI is a production-ready Multi AI Agents framework, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. It provides a low-code solution to streamline the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.

A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)

Relax! Flux is the ML library that doesn't make you tensor

Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets

The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"

Foundational model for human-like, expressive TTS