AliaksandrSiarohin/first-order-model
15,003 stars·3,273 forks·Jupyter Notebook·MIT·26 viewsaliaksandrsiarohin.github.io/first-order-model-website↗
First Order 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 system utilizes dense motion field estimation and local affine transformations to warp source image features into target poses. Through an encoder-decoder architecture and adversarial training, it generates realistic video frames that map facial expressions and head movements from a source video onto a target subject.
Features
- Portrait Animation Tools - Maps facial expressions and head movements from a source video onto a target image for synthetic media production.
- Generative Adversarial Image Synthesis - Transfers motion patterns from a driving video onto a static source image to synthesize realistic video sequences.
- Keypoint-Based Motion Transfer Models - Decouples object appearance from movement by tracking structural deformations through learned latent keypoints.
- Image-to-Video Animators - Generates video sequences by applying motion patterns from a driving video to a static source image.
- Generative Adversarial Networks - Synthesizes high-quality video frames by learning the underlying structure and motion dynamics of visual objects.
- Portrait Video Retargeting - Transfers complex movement sequences from one video source to another subject to animate characters or faces.
- Generative Image Models - Transfers motion patterns from a driving video onto a static source image to generate realistic video sequences.
- Deepfake Generation - Maps facial expressions and head movements of a source person onto a target image to create realistic synthetic videos.
- Keypoint-Based Motion Representations - Tracks structural deformations through learned latent coordinates to decouple object appearance from temporal movement patterns.
- Computer Vision Libraries - Image animation using motion models.
- Facial Manipulation - Motion models for image animation and synthesis.
- Human Generation and Synthesis - Image animation using first-order motion models.
- Image Driven Animation - Standard motion model for image animation using keypoints.
- Video and Motion Synthesis - Motion modeling for image animation and reenactment.
- Audio and Subtitle Tools - Deep learning model for image and portrait animation.
- Portrait Animation Engines - Transforms a single static portrait into a moving video by applying motion patterns extracted from a driving video.
- Unsupervised Motion Transfer Frameworks - Performs unsupervised motion transfer and image manipulation tasks using deep learning models without manual annotations.
- Synthetic Media Generators - Generates expressive video content from static assets for creative projects and digital avatars.
- Dense Motion Field Estimators - Computes pixel-wise displacement vectors to align source image textures with the geometry of driving video frames.
Star history
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