Musa Deniev's starred repositories
photo-editor
Photo editor with a lot of cool features
photo-editor-luts-swiftui
🎨 An image editor using SwifUI & muukii/Pixel
MetalViewUI
SwiftUI view wrapper for MTKView.
TinyCrayon-iOS-SDK
A smart and easy-to-use image masking and cutout SDK for mobile apps.
6amMart-delivery-app
6amMart-delivery-app
whatsapp-web.js
A WhatsApp client library for NodeJS that connects through the WhatsApp Web browser app
MetalPetal
A GPU accelerated image and video processing framework built on Metal.
ZLImageEditor
A powerful image editor framework. Supports graffiti, cropping, mosaic, text stickers, image stickers, filters.
MediaEditor-iOS
Easy add image editing features to your iOS app! 🖼️
swift-composable-architecture
A library for building applications in a consistent and understandable way, with composition, testing, and ergonomics in mind.
BackgroundMusic
Background Music, a macOS audio utility: automatically pause your music, set individual apps' volumes and record system audio.
gpt4all-chat
gpt4all-j chat
compose-multiplatform-ios-android-template
Compose Multiplatform iOS+Android Application project template
ios-twitter-logging-service
Twitter Logging Service is a robust and performant logging framework for iOS clients
ios_system
Drop-in replacement for system() in iOS programs
HorizonCalendar
A declarative, performant, iOS calendar UI component that supports use cases ranging from simple date pickers all the way up to fully-featured calendar apps.
swift-composable-navigation
Models UI navigation patterns using TCA
MocapNET
We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance