Enhancing Gaze Estimation with Neural Networks and Synthetic Images

Alapfogalmak

The authors utilize neural networks and synthetic images to enhance gaze estimation for human-robot interaction, achieving high accuracy without the need for special hardware.

Kivonat

The content discusses the importance of accurate gaze estimation in human-robot interaction. The authors propose a method using neural networks that estimate gaze from cropped eyes, leveraging existing components like RetinaFace and 6DRepNet. They introduce a large synthetic dataset generated with MetaHuman tool, leading to improved accuracy in eye pitch and yaw directions. The system works effectively with standard RGB cameras, demonstrating feasibility in real-world settings. Various datasets are compared, highlighting the benefits of combining datasets for more generalized models. The study also includes details on model building, dataset testing, and real-world applications.

Statisztikák

Our proposed method led to better accuracy with a mean average error below two degrees in eye pitch and yaw directions.
A dataset of 57,375 human faces was generated using MetaHuman tool.
Model trained on MetaHuman data performs best in real-world testing scenarios.
MAE improved to approximately 1.9° after expanding the training data by image mirroring.
The final architecture had 2,092,342 trainable parameters.

Idézetek

"Our proposed method does not require any special hardware or infrared filters but uses a standard notebook-builtin RGB camera."
"Using this data significantly improved the accuracy of our model compared to only using a smaller training dataset."
"The most stable model across all three datasets is the one trained on the combined dataset."
"Our model has a problem with stability in worse lighting conditions."
"We plan to use the model in our HRI research on intention reading by a robot."

Főbb Kivonatok

Appearance-based gaze estimation enhanced with synthetic images using deep neural networks

by Dmyt... : arxiv.org 03-01-2024

https://arxiv.org/pdf/2311.14175.pdf

Mélyebb kérdések

How can combining different datasets improve the generalization of models

Combining different datasets can significantly improve the generalization of models in gaze estimation. By merging diverse datasets like the Columbia Gaze dataset and the MetaHuman dataset, a model gains exposure to a wider range of scenarios, lighting conditions, head poses, and eye positions. This diversity helps the model learn more robust features that are applicable across various real-world situations. Additionally, combining datasets increases the variability in training data, reducing overfitting to specific characteristics present in individual datasets. As a result, the model becomes more adept at handling unseen data and performs better when faced with new environments or conditions.

What challenges may arise when predicting eye gaze directions under varying conditions

Predicting eye gaze directions under varying conditions presents several challenges for gaze estimation systems. One significant challenge is dealing with changes in lighting conditions that can affect image quality and alter the appearance of key features like pupils and eyelids. Inadequate lighting may lead to inaccuracies in estimating gaze direction due to poor visibility of crucial details within the eyes. Another challenge arises when individuals look down or away from the camera, causing their eye pupils to be less visible or obscured by other facial features such as eyebrows or glasses. This situation makes it harder for models to accurately predict eye gaze without clear visual cues from both eyes.

How can generative AI tools like MetaHuman impact future research in gaze estimation

Generative AI tools like MetaHuman have a profound impact on future research in gaze estimation by enabling researchers to create large-scale synthetic datasets efficiently and cost-effectively. These tools provide researchers with access to high-quality digital human models that can be manipulated to generate diverse images representing various head poses, eye positions, facial expressions, and lighting conditions. By leveraging these generative capabilities, researchers can expand their training data significantly beyond what is feasible through traditional data collection methods alone.
The use of tools like MetaHuman allows for controlled generation of complex visual scenarios that closely mimic real-world settings while offering flexibility in adjusting parameters such as head orientation and illumination levels systematically.
Moreover,
these synthetic datasets serve as valuable resources for training deep learning models used in gaze estimation tasks where collecting extensive real-world data would be challenging or impractical.
By utilizing generative AI tools effectively,
researchers can enhance model performance,
generalizability,
and robustness by exposing them
to a wide array of simulated but realistic scenarios during training phases