Open-source alternatives to CVPR2022 DaGAN

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CVPR 2022 Structure-Aware Motion Transfer with Deformable Anchor Model

HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation Control

LivePortrait is a deep learning framework for portrait animation that transfers facial expressions from a driving video to a static image. It functions as an AI motion retargeting tool, mapping movements between different identities while preserving the unique features of the source portrait. The system includes specialized capabilities for cross-species portrait animation, adapting human-centric models to non-human subjects and animals. It also features a motion template generator that converts driving videos into portable files to accelerate inference and protect the identity of the origina

Follow-Your-Emoji Fine-Controllable and Expressive Freestyle Portrait Animation

Official github repo for Audio Visual Facial Reenactment (WACV 2023). For now, we have put the public release of code on hold due to licensing and ethical concerns. Feel free to reach out for any specific queries!

Official implementation of EMOPortraits: Emotion-enhanced Multimodal One-shot Head Avatars.

Note This repo is now deprecated. Please refer to the new Imaginaire repo: https://github.com/NVlabs/imaginaire.

Pytorch code for ECCV 2018 paper

Official Pytorch Implementation of 3DV2021 paper: SAFA: Structure Aware Face Animation.

| ArXiv | Get Start | Video

Code for Motion Representations for Articulated Animation paper

Authors official PyTorch implementation of StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment. This paper has been accepted for publication at IEEE Conference on Automatic Face and Gesture Recognition, 2023.

My implementation of Few-Shot Adversarial Learning of Realistic Neural Talking Head Models (Egor Zakharov et al.). https://arxiv.org/abs/1905.08233

Yaohui Wang, Di Yang, François Brémond, Antitza Dantcheva This is the official PyTorch implementation of the ICLR 2022 paper "Latent Image Animator: Learning to Animate Images via Latent Space Navigation" and TPAMI 2024 paper "LIA: Latent Image Animator".

Created by Wayne Wu, Yunxuan Zhang and Ren Li.

This project is a generative adversarial network designed for image animation and motion transfer. It functions as a computer vision framework that synthesizes video sequences by applying motion patterns extracted from a driving video onto a static source image. The model distinguishes itself by using a keypoint-based representation to decouple object appearance from temporal movement. By tracking structural deformations through learned latent coordinates, it performs motion retargeting and synthetic media production without requiring manual annotations or object-specific training data. The

Source code of the CVPR'2022 paper "Thin-Plate Spline Motion Model for Image Animation"