HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation Control
Las características principales de kkakkkka/hunyuanportrait son: Controllable Generation, Image Driven Animation.
Las alternativas de código abierto para kkakkkka/hunyuanportrait incluyen: bytedance/x-portrait — X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention You Xie , Hongyi Xu , Guoxian Song , Chao… kwaivgi/liveportrait — LivePortrait is a deep learning framework for portrait animation that transfers facial expressions from a driving… winfredy/sadtalker — SadTalker is a generative framework designed to synthesize expressive talking head videos from static portrait images.… ali-vilab/unianimate-dit. alibaba/tora — [CVPR'25]Tora: Trajectory-oriented Diffusion Transformer for Video Generation. aliaksandrsiarohin/monkey-net — This repository contains the source code for the CVPR oral paper Animating Arbitrary Objects via Deep Motion Transfer…
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