Hopenet is an accurate and easy to use head pose estimation network. Models have been trained on the 300W-LP dataset and have been tested on real data with good qualitative performance.
For details about the method and quantitative results please check the CVPR Workshop paper.
new Conan-Cruise-Car example video
To use please install PyTorch and OpenCV (for video) - I believe that's all you need apart from usual libraries such as numpy. You need a GPU to run Hopenet (for now).
To test on a video using dlib face detections (center of head will be jumpy):
python code/test_on_video_dlib.py --snapshot PATH_OF_SNAPSHOT --face_model PATH_OF_DLIB_MODEL --video PATH_OF_VIDEO --output_string STRING_TO_APPEND_TO_OUTPUT --n_frames N_OF_FRAMES_TO_PROCESS --fps FPS_OF_SOURCE_VIDEOTo test on a video using your own face detections (we recommend using dockerface, center of head will be smoother):
python code/test_on_video_dockerface.py --snapshot PATH_OF_SNAPSHOT --video PATH_OF_VIDEO --bboxes FACE_BOUNDING_BOX_ANNOTATIONS --output_string STRING_TO_APPEND_TO_OUTPUT --n_frames N_OF_FRAMES_TO_PROCESS --fps FPS_OF_SOURCE_VIDEOFace bounding box annotations should be in Dockerface format (n_frame x_min y_min x_max y_max confidence).
Pre-trained models:
300W-LP, alpha 1, robust to image quality
For more information on what alpha stands for please read the paper. First two models are for validating paper results, if used on real data we suggest using the last model as it is more robust to image quality and blur and gives good results on video.
Please open an issue if you have an problem.
Some very cool implementations of this work on other platforms by some cool people:
If you find Hopenet useful in your research please cite:
@InProceedings{Ruiz_2018_CVPR_Workshops,
author = {Ruiz, Nataniel and Chong, Eunji and Rehg, James M.},
title = {Fine-Grained Head Pose Estimation Without Keypoints},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}
Nataniel Ruiz, Eunji Chong, James M. Rehg
Georgia Institute of Technology