Abstract: Aspects of hair simulation, and networks therefor are provided including aspects to train such networks. There is provided a generative model for hair simulation that is guided during training by a hair classifier model. The generative model in an embodiment is provided for use in a virtual try-on (VTO) pipeline such as for virtually trying on hair color products. Further provided is a color mapping network to process an input image and target hair color for the generative model to define the hair simulation (e.g. as an output image with simulated hair color).

Abstract: Methods and devices for machine vision-based selection of content are described. One or more hands are detected in a current frame of video data. A respective fingertip location is determined for each of up to two of the detected hands. A content selection gesture is determined corresponding to the up to two detected hands. Selected content is extracted, as indicated by the content selection gesture and based on the up to two fingertip locations. The device may be a smartphone, a tablet, a laptop, a smart light device, a reader device, etc.

Abstract: Method and devices for training a keypoint estimation network are described. In each training iteration, synthetic images are generated by a generator, each synthetic image being assigned respective assigned keypoints by the generator. Using a prior-iteration of the keypoint estimation network, a set of predicted keypoints is obtained for each synthetic image. Based on an error score between the predicted keypoints and the assigned keypoints, poor quality synthetic images are discarded. The remaining synthetic images, together with real world images, are used to train an updated keypoint estimation network. The performance of the updated keypoint estimation network is validated, and the training iterations are performed until a convergence criteria is satisfied.

Abstract: Aspects of hair simulation, and networks therefor are provided including aspects to train such networks. There is provided a generative model for hair simulation that is guided during training by a hair classifier model. The generative model in an embodiment is provided for use in a virtual try-on (VTO) pipeline such as for virtually trying on hair color products. Further provided is a color mapping network to process an input image and target hair color for the generative model to define the hair simulation (e.g. as an output image with simulated hair color).

Abstract: Aspects of hair classification, and networks therefor are provided including aspects to train such networks. There is provided a classifier model to alleviate the impact of human bias where the modeling of the real label distribution and annotators' biases are separated by incorporating annotator confusion matrices into a baseline model. To further improve the model performance leveraging unlabeled data, the model was trained using a consistency-based semi-supervised learning framework. With the use of only 1000 labeled data, the final classifier model achieved a classification accuracy that was 20% higher than a human professional annotator. The trained model can be used for a wide range of downstream tasks, including being used as a color classifier to train generative models for hair color translation.

Abstract: Methods and devices for machine vision-based selection of content are described. One or more hands are detected in a current frame of video data. A respective fingertip location is determined for each of up to two of the detected hands. A content selection gesture is determined corresponding to the up to two detected hands. Selected content is extracted, as indicated by the content selection gesture and based on the up to two fingertip locations. The device may be a smartphone, a tablet, a laptop, a smart light device, a reader device, etc.

Abstract: Method and devices for training a keypoint estimation network are described. In each training iteration, synthetic images are generated by a generator, each synthetic image being assigned respective assigned keypoints by the generator. Using a prior-iteration of the keypoint estimation network, a set of predicted keypoints is obtained for each synthetic image. Based on an error score between the predicted keypoints and the assigned keypoints, poor quality synthetic images are discarded. The remaining synthetic images, together with real world images, are used to train an updated keypoint estimation network. The performance of the updated keypoint estimation network is validated, and the training iterations are performed until a convergence criteria is satisfied.