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Les fonctionnalités principales de renyurui/pirender sont : Image Driven Animation, Faces:.
Les alternatives open-source à renyurui/pirender incluent : winfredy/sadtalker — SadTalker is a generative framework designed to synthesize expressive talking head videos from static portrait images.… aliaksandrsiarohin/monkey-net — This repository contains the source code for the CVPR oral paper Animating Arbitrary Objects via Deep Motion Transfer… anhttran/3dmm_basic. anhttran/3dmm_cnn. anhttran/extreme_3d_faces. aliaksandrsiarohin/first-order-model — This project is a generative adversarial network designed for image animation and motion transfer. It functions as a…
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…
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