Open-source alternatives to HunyuanPortrait

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

X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention You Xie , Hongyi Xu , Guoxian Song , Chao Wang , Yichun Shi , Linjie Luo ByteDance Inc.

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

CVPR'25Tora: Trajectory-oriented Diffusion Transformer for Video Generation

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…

Champ is a generative vision system and controllable image-to-video generator designed for human image animation. It uses a diffusion-based video synthesizer and 3D parametric guidance to transform a single reference image into a consistent sequence of motion based on external driving data. The framework distinguishes itself through a human pose transfer system that employs 3D body parametric extraction and coordinate-space alignment. This allows the model to map motion from a driving video to a reference person by adjusting for body scales and camera perspectives using depth and semantic con

paper | project website

Expressive Portrait Image Animation for Live Streaming

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AnimateAnyone is an appearance-preserving video synthesizer designed for character animation from a single static image. It functions as a diffusion image-to-video generator that transforms a source image into a high-fidelity video sequence while maintaining consistent character identity, clothing, and visual details across all frames. The system enables video-driven character reenactment by transferring motions, facial expressions, and body movements from a reference video onto a static character. It employs pose-guided video generation to control movement via skeleton keypoints and pose sig

CVPR 2022 Structure-Aware Motion Transfer with Deformable Anchor Model

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