6DoF Head Pose Estimation through Explicit Bidirectional Interaction with Face Geometry

🌈 Abstract

This study addresses the challenge of estimating head translations within the context of six-degrees-of-freedom (6DoF) head pose estimation, placing emphasis on this aspect over the more commonly studied head rotations. The authors propose a novel approach called the head Translation, Rotation, and face Geometry network (TRG), which stands out for its explicit bidirectional interaction structure that leverages the complementary relationship between face geometry and head translation. The authors also develop a strategy for estimating bounding box correction parameters and a technique for aligning landmarks to the image, which demonstrate superior performance in 6DoF head pose estimation tasks. Extensive experiments on ARKitFace and BIWI datasets confirm that the proposed method outperforms current state-of-the-art techniques.

🙋 Q&A

[01] Overview of the Proposed Method

1. What is the key feature of the proposed TRG method? The key feature of the proposed TRG method is its explicit bidirectional interaction structure that leverages the complementary relationship between face geometry and head translation.

2. What are the two main challenges in estimating head translation from a single image using learning-based methods? The two main challenges are:

• Head translation estimation depends on real-scale face geometry, but the estimation of real-scale face geometry suffers from head translation ambiguities.
• Learning-based head translation estimation encounters severe generalization issues with out-of-distribution data, as the range of head translation is infinite.

3. How does TRG address the limitations of existing models? To overcome the limitations of existing models, TRG:

• Proposes an explicit bidirectional interaction structure that leverages the complementary characteristics between the 6DoF head pose and face geometry.
• Utilizes the position and size information of the bounding box to estimate head translation, rather than directly estimating depth.
• Estimates bounding box correction parameters to address the discrepancies between the bounding box center/size and the actual head center/depth.
• Aligns the estimated 3D landmarks with the image through perspective projection, which enhances the performance of head translation and rotation estimation.

4. What are the key contributions of this study? The key contributions are:

1. Proposing the TRG method with an explicit bidirectional interaction structure between head translation and face geometry.
2. Developing a strategy for estimating bounding box correction parameters to achieve stable generalization performance on out-of-distribution data.
3. Demonstrating a landmark-to-image alignment strategy that achieves high accuracy in both head translation and rotation estimation.
4. Showing that TRG's depth-aware landmark prediction architecture exhibits high landmark prediction accuracy, even in images heavily influenced by perspective transformation.

[02] Experimental Results

1. What are the key findings from the ablation experiments? The key findings from the ablation experiments are:

• The performance in estimating face geometry and head pose improves with the increasing number of bidirectional interactions between the 6DoF head pose and face geometry.
• Estimating the bounding box correction parameters instead of directly estimating head translation significantly enhances the model's generalizability, particularly for data that fall outside the training distribution.
• Incorporating facial geometry information and using the landmark-to-image alignment technique improves the 6DoF head pose estimation performance compared to landmark-free approaches.

2. How does TRG compare to the state-of-the-art methods on the ARKitFace and BIWI datasets? On the ARKitFace dataset:

• TRG outperforms existing methods in both head pose estimation and face landmark prediction accuracy.
• This is attributed to TRG's explicit bidirectional interaction structure and its depth-aware landmark prediction architecture.

On the BIWI dataset:

• TRG significantly outperforms existing optimization-based methods in head translation estimation.
• TRG also achieves high head rotation estimation accuracy, surpassing even methods that solely estimate 3D head rotation.

3. What are the limitations of the proposed method? The proposed method requires camera intrinsic parameters to derive depth from images. In the absence of camera intrinsics, while it is still possible to estimate relative depth among faces in an image, achieving precise depth measurement poses a challenge. Incorporating algorithms that estimate intrinsics becomes essential when intrinsic parameters are not readily available.

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