US 12,430,905 B2
Methods, devices, and computer readable media for training a keypoint estimation network using cGAN-based data augmentation
Xin Ding, Saint-Laurent (CA); Deepak Sridhar, Saint-Laurent (CA); Juwei Lu, Saint-Laurent (CA); Sidharth Singla, Saint-Laurent (CA); Peng Dai, Saint-Laurent (CA); and Xiaofei Wu, Guangdong (CN)
Assigned to HUAWEI TECHNOLOGIES CO., LTD., Shenzhen (CN)
Filed by Huawei Technologies Co., Ltd., Guangdong (CN)
Filed on May 11, 2023, as Appl. No. 18/315,866.
Application 18/315,866 is a continuation of application No. PCT/CN2021/092938, filed on May 11, 2021.
Prior Publication US 2023/0281981 A1, Sep. 7, 2023
Int. Cl. G06V 10/82 (2022.01); G06V 10/776 (2022.01)
CPC G06V 10/82 (2022.01) [G06V 10/776 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method for training a keypoint estimation network, the method comprising:
performing a plurality of training iterations, each training iteration comprising:
obtaining a set of synthetic images generated by a generator, each synthetic image being assigned a respective set of assigned keypoints by the generator;
using a prior-iteration keypoint estimation network, obtaining a set of predicted keypoints for each synthetic image;
based on computation of an error score between the set of predicted keypoints and the respective set of assigned keypoints for each respective synthetic image:
identifying and discarding any synthetic image having an error score that fails a preset threshold; and
identifying and adding, to a synthetic dataset, any synthetic image having an error score that satisfies the preset threshold;
training an updated keypoint estimation network, using a combined dataset comprising the synthetic dataset combined with a real world dataset containing real world images; and
computing a mean error score for the updated keypoint estimation network, the mean error score representing performance of the updated keypoint estimation network on a validation dataset;
wherein the training iterations are performed until a convergence criteria is satisfied; and
storing the updated keypoint estimation network from a final training iteration as a final keypoint estimation network.