The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Fine-Grained Head Pose Estimation Without Keypoints
Open accessAbstract
Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment. Traditionally head pose is computed by estimating some keypoints from the target face and solving the 2D to 3D correspondence problem with a mean human head model. We argue that this is a fragile method because it relies entirely on landmark detection performance, the extraneous head model and an ad-hoc fitting step. We present an elegant and robust way to determine pose by training a multi-loss convolutional neural network on 300W-LP, a large synthetically expanded dataset, to predict intrinsic Euler angles (yaw, pitch and roll) directly from image intensities through joint binned pose classification and regression. We present empirical tests on common in-the-wild pose benchmark datasets which show state-of-the-art results. Additionally we test our method on a dataset usually used for pose estimation using depth and start to close the gap with state-of-the-art depth pose methods. We open-source our training and testing code as well as release our pre-trained models.
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SimSwap: An Efficient Framework For High Fidelity Face Swapping
Chen, R., Chen, X., Ni, B., & Ge, Y. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413630 · Cited by 418
Zhang, W., Cun, X., Wang, X., Zhang, Y., Shen, X., Guo, Y., Shan, Y., & Wang, F. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00836 · Cited by 336
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Li, L., Bao, J., Yang, H., Chen, D., & Wen, F. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00512 · Cited by 288
Metadata
- Authors
- Ruiz, N., Chong, E., & Rehg, J. M.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 2155-215509
- OpenAlex
- W2761173690
- Cited by
- 631
- References
- 35
- FWCI
- 18.85
- Type
- conference paper
- Language
- en
- Open access
- green
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- Ruiz, N., Chong, E., & Rehg, J. M. (2018). Fine-Grained Head Pose Estimation Without Keypoints. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw.2018.00281
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