Practical overview
When it fits
Use Hopenet when you need to run head-pose estimation on video and can meet its GPU requirement, install the documented Python libraries, and provide face detections through one of the supported paths.
Who it is for
- Researchers studying head-pose estimation and the cited CVPR Workshops method.
- Developers building video-processing workflows that need head-pose estimates and can provide face detections or bounding-box annotations.
Documented uses
- Estimate head pose from recorded video using dlib face detections.
- Process video with externally generated face bounding boxes in Dockerface format.
- Compare estimates across the repository's pretrained model variants.
Requirements & limits
- A GPU is required according to the README.
- Video processing requires OpenCV; PyTorch and usual libraries such as NumPy are also listed.
- The dlib script requires a dlib model and may produce a jumpy head center.
- The supplied-box script requires annotations in Dockerface format; the README recommends this path for a smoother center.
- The repository recommends the image-quality-robust pretrained model for real data and video; it describes the other two as useful for validating paper results.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Process video with dlib face detections
Run the repository's documented dlib-based video inference script.
Before you start
- PyTorch and OpenCV for video, plus usual libraries such as NumPy.
- A GPU, as required by the README.
- A pretrained model snapshot, a dlib face model and an input video.
Steps
Obtain a pretrained model snapshot from one of the model links listed in the README.
Provide the snapshot, dlib model and video to code/test_on_video_dlib.py using its documented arguments.
Set the output-string, frame-count and source-fps arguments for the invocation.
Expected outcome
The script processes the video using dlib face detections for head-pose estimation.
Guide limitations
- The README warns that the center of the head can be jumpy with dlib detections.
- The guide describes the documented invocation and does not establish execution results.
Process video with supplied face boxes
Run the repository's video script with externally supplied face bounding-box annotations.
Before you start
- PyTorch and OpenCV for video, plus usual libraries such as NumPy.
- A GPU, as required by the README.
- A pretrained model snapshot, input video and face-box annotations in Dockerface format: frame number, x_min, y_min, x_max, y_max and confidence.
Steps
Obtain a pretrained model snapshot from one of the model links listed in the README.
Prepare face-box annotations in the specified Dockerface format.
Provide the snapshot, video and annotations to code/test_on_video_dockerface.py using its documented arguments.
Set the output-string, frame-count and source-fps arguments for the invocation.
Expected outcome
The script processes the video using the supplied face boxes for head-pose estimation.
Guide limitations
- The README recommends this route for a smoother head center than dlib detections.
- The guide describes the documented invocation and does not establish execution results.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- natanielruiz/deep-head-pose · Official homepage title · retained in its source language
Related options
Gluon implementation — The README links to an implementation of the work on Gluon.
MXNet implementation — The README links to an implementation of the work on MXNet.
TensorFlow with Keras implementation — The README links to an implementation of the work using TensorFlow with Keras.
Deep Head Pose Light — The README identifies it as a lightweight version of Hopenet.