24 open-source projects similar to vincent-thevenin/realistic-neural-talking-head-models, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Realistic Neural Talking Head Models alternative.
SadTalker is a generative framework designed to synthesize expressive talking head videos from static portrait images. By mapping audio signals or text prompts to three-dimensional facial motion coefficients, the system synchronizes lip movements, facial expressions, and head orientation to create realistic digital character performances. The project distinguishes itself by decoupling identity from dynamic motion through latent space encoding, ensuring that the generated animations maintain visual fidelity to the source portrait. It supports comprehensive motion synthesis, including full-body
This repository contains the source code for the CVPR oral paper Animating Arbitrary Objects via Deep Motion Transfer by Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci and Nicu Sebe. We call the proposed deep framework Monkey-Net, as it enables motion transfer by…
X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention You Xie , Hongyi Xu , Guoxian Song , Chao Wang , Yichun Shi , Linjie Luo ByteDance Inc.
Expressive Portrait Image Animation for Live Streaming
:fire: If DaGAN is helpful in your photos/projects, please help to :star: it or recommend it to your friends. Thanks:fire: :fire: Seeking for the collaboration and internship opportunities. :fire:
:fire: If MCNet is helpful in your photos/projects, please help to :star: it or recommend it to your friends. Thanks:fire:
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.
Official Pytorch Implementation of 3DV2021 paper: SAFA: Structure Aware Face Animation.
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.
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".
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"