PyTorch-Pose is a PyTorch implementation of the general pipeline for 2D single human pose estimation. The aim is to provide the interface of the training/inference/evaluation, and the dataloader with various data augmentation options for the most popular human pose databases (e.g., the MPII…
The main features of bearpaw/pytorch-pose are: Model Implementations, Pose Estimation Frameworks.
Open-source alternatives to bearpaw/pytorch-pose include: xingyizhou/pytorch-pose-hg-3d — This repository is the PyTorch implementation for the network presented in:. mkocabas/vibe — VIBE is a 3D human pose estimation framework designed to reconstruct human body shapes and poses from video frames. It… facebookresearch/animateddrawings — AnimatedDrawings is a system for transforming static 2D drawings of humanoid figures into animated characters. It… meta-llama/llama-cookbook — This project is a collection of implementation guides, recipes, and developer resources for building applications with… agermanidis/thingscoop — Search and filter videos based on objects that appear in them using convolutional neural networks. aaron-xichen/pytorch-playground — Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet,…
This repository is the PyTorch implementation for the network presented in:
VIBE is a 3D human pose estimation framework designed to reconstruct human body shapes and poses from video frames. It functions as a toolkit for predicting parameters of the SMPL human body model to generate 3D mesh sequences. The system includes a 3D motion data exporter to convert predicted pose sequences into standard 3D file formats for use in graphics and animation software. It also provides a structured training pipeline for preparing datasets and training models to estimate body shapes from images. Its capabilities cover computer vision for estimating body pose and shape, as well as
AnimatedDrawings is a system for transforming static 2D drawings of humanoid figures into animated characters. It provides a workflow for character rigging, motion mapping, and scene assembly to turn hand-drawn sketches into moving visual sequences. The project utilizes a motion retargeting framework to map movement data from motion capture files onto custom character skeletons regardless of their physical proportions. It employs a rigging tool that uses pose estimation to automatically predict joint locations and create digital skeletons, which can be manually refined to improve animation ac
This project is a collection of implementation guides, recipes, and developer resources for building applications with Llama models. It serves as a comprehensive kit for developing autonomous agents, establishing retrieval-augmented generation systems, and executing model fine-tuning. The resource provides specific patterns for multimodal workflows that process text, images, and audio. It includes specialized guidance on adapting pre-trained model weights for targeted tasks and implementing tool-calling orchestration to connect models with external APIs and functions. The codebase covers a b