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vincent-thevenin/Realistic-Neural-Talking-Head-Models

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831 Stars·191 Forks·Python·GPL-3.0·7 Aufrufe

Realistic Neural Talking Head Models

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

Features

  • Image Driven Animation - Few-shot adversarial learning for realistic talking head models.

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Häufig gestellte Fragen

Was macht vincent-thevenin/realistic-neural-talking-head-models?

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

Was sind die Hauptfunktionen von vincent-thevenin/realistic-neural-talking-head-models?

Die Hauptfunktionen von vincent-thevenin/realistic-neural-talking-head-models sind: Image Driven Animation.

Welche Open-Source-Alternativen gibt es zu vincent-thevenin/realistic-neural-talking-head-models?

Open-Source-Alternativen zu vincent-thevenin/realistic-neural-talking-head-models sind unter anderem: winfredy/sadtalker — SadTalker is a generative framework designed to synthesize expressive talking head videos from static portrait images.… bytedance/x-portrait — X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention You Xie , Hongyi Xu , Guoxian Song , Chao… feiiyin/styleheat — paper | project website. gvclab/personalive — Expressive Portrait Image Animation for Live Streaming. harlanhong/cvpr2022-dagan — :fire: If DaGAN is helpful in your photos/projects, please help to :star: it or recommend it to your friends.… aliaksandrsiarohin/monkey-net — This repository contains the source code for the CVPR oral paper Animating Arbitrary Objects via Deep Motion Transfer…

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    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

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    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…

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