albertpumarolaGANimation
1,984 stele403 fork-uriPythonGPL-3.03 vizualizăriwww.albertpumarola.com/research/GANimation/index.html
Official implementation of GANimation. In this work we introduce a novel GAN conditioning scheme based on Action Units (AU) annotations, which describe in a continuous manifold the anatomical facial movements defining a human expression. Our approach permits controlling the magnitude of…
Principalele funcționalități ale albertpumarola/ganimation sunt: Computer Vision Research, Facial Manipulation, Generative Face Models, Facial, Action and Pose Recognition.
Alternativele open-source pentru albertpumarola/ganimation includ: yunjey/stargan — StarGAN is a PyTorch image-to-image translation framework designed to synthesize visual styles and attributes across… iperov/deepfacelab — DeepFaceLab is a deep learning software suite designed for face swapping and the creation of deepfake videos. It… kenshohara/3d-resnets-pytorch — This project is a PyTorch implementation of 3D residual networks designed for video action recognition. It provides a… run-youngjoo/sc-fegan — SC-FEGAN : Face Editing Generative Adversarial Network with User's Sketch and Color (ICCV 2019). prinsphield/elegant — Taihong Xiao, Jiapeng Hong and Jinwen Ma. alievk/avatarify-python — Avatarify-python is a real-time face animation tool that uses a PyTorch-based neural network to map facial movements…
StarGAN is a PyTorch image-to-image translation framework designed to synthesize visual styles and attributes across multiple domains. It implements a generative adversarial network that serves as a deep learning image translator for modifying specific visual characteristics within an image dataset. The framework uses a single unified model to handle translations between multiple image domains rather than requiring separate pairs of models. It is a research implementation that learns mappings between different image attributes without the need for paired training data. The project covers the
DeepFaceLab is a deep learning software suite designed for face swapping and the creation of deepfake videos. It functions as a neural network image compositor that replaces human faces or entire heads in video files to produce synthetic media. The tool provides capabilities for digital facial manipulation, including the ability to modify the perceived age of people in video sequences. It uses automated pattern recognition to blend source faces onto target frames to create seamless visual composites. The system covers a broad technical surface including landmark-based face alignment, autoenc
This project is a PyTorch implementation of 3D residual networks designed for video action recognition. It provides a spatiotemporal architecture that analyzes both spatial frames and temporal motion to classify human activities within video clips. The system includes a distributed model training framework to accelerate learning across multiple compute nodes. It supports the deployment and fine-tuning of pre-trained model weights, allowing the adaptation of existing networks to specific new datasets. The codebase covers the full pipeline for spatiotemporal learning, including video dataset p