SO(3)-based Continuity Representation to Intensify Head Pose Estimation
2023 8th International Conference on Computational Intelligence and Applications (ICCIA)(2023)
Abstract
In this paper, We proposed an improved succinct structure for head pose estimation from a single image while maintaining continuous representation. which transforms the Head Pose Estimation (HPE) problem into a problem of directly predicting continuous 6D rotation matrix parameters belongs 3D Special Orthogonal Group (SO(3)). The model uses the lighter RepVGG-A2 as the backbone, and the improved Root Mean Square Error of Geodesic Distance(RSME-GD) as the loss function. Experiments show that compared with the original SOTA method, our model parameters are reduced by about 40%, the training speed is 6X faster and the results are even better.
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Key words
head pose estimation,SO(3),geodesic distance,rotation matrix
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