hysts/pytorch_mpiigaze_demo

Gaze estimation using MPIIGaze and MPIIFaceGaze

What it solves

This project provides a demo program for gaze estimation, allowing users to determine where a person is looking based on images or video feeds (including live webcam input). It simplifies the process of using pretrained gaze estimation models like MPIIGaze, MPIIFaceGaze, and ETH-XGaze.

How it works

The program uses deep learning models to analyze visual data. It first detects faces and landmarks using a chosen detector (such as MediaPipe or dlib), then applies a

Who it's for

This tool is designed for developers and researchers interested in human-computer interaction, computer vision, and gaze tracking applications.

Highlights

  • Multiple Model Support: Supports MPIIGaze, MPIIFaceGaze, and ETH-XGaze models.

  • Flexible Input: Works with static images, video files, or live webcam streams.

  • Customizable Detection: Offers multiple face detection options including MediaPipe, dlib, and face_alignment_sfd.

  • Pretrained Weights: Automatically downloads weights from the Hugging Face Hub in safetensors format.

  • Visualization Tools: Includes interactive keys to toggle the display of the display of landmarks, head pose, and bounding boxes during processing.

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