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With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.
#4 best model for Image-to-Image Translation on Cityscapes Photo-to-Labels
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.
SOTA for Head Pose Estimation on BIWI
We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task.
We first record a novel dataset of varied gaze and head pose images in a natural environment, addressing the issue of ground truth annotation by measuring head pose using a motion capture system and eye gaze using mobile eyetracking glasses.
SOTA for Gaze Estimation on UT Multi-view
Second, we present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze.
Gaze behavior is an important non-verbal cue in social signal processing and human-computer interaction.
In this work, we present a novel method to alleviate this problem by leveraging generative adversarial training to synthesize an eye image conditioned on a target gaze direction.
Automatic eye gaze estimation has interested researchers for a while now.
We believe that we can put the power of eye tracking in everyone's palm by building eye tracking software that works on commodity hardware such as mobile phones and tablets, without the need for additional sensors or devices.
nature of this data suggests better estimation may be possible if the model explicitly made use of such "repeated measurements" from each user as is commonly done in classical statistical analysis using so-called mixed effects models.