Abstract: A method may include obtaining a facial image of a subject and identifying a number of new images to be synthesized with target AU combinations and categories of intensity. The method may also include synthesizing the number of new images using the facial image of the subject as the base image with the number of target AU combinations and categories of intensity with a number of new images that have different AU combinations than the facial image of the subject. The method may additionally include adding the number of new images to a dataset and training a machine learning system using the dataset to identify a facial expression of the subject.

Abstract: A method may include obtaining a dataset including a target Action Unit (AU) combination and labeled images of the target AU combination with at least a first category of intensity for each AU of the target AU combination and a second category of intensity for each AU of the target AU combination. The method may also include determining that the first category of intensity for a first AU has a higher number of labeled images than the second category of intensity for the first AU, and based on the determination, identifying a number of new images to be synthesized in the second category of intensity for the first AU. The method may additionally include synthesizing the number of new images with the second category of intensity for the first AU, and adding the new images to the dataset.

Abstract: Computer vision systems and methods for high-fidelity representation of complex 3D surfaces using deep unsigned distance embeddings are provided. The system receives data associated with the 3D surface. The system processes the data based at least in part on one or more computer vision models to predict an unsigned distance field and a normal vector field. The unsigned distance field is indicative of proximity to the 3D surface and includes a predicted closest unsigned distance to a surface point of the 3D surface from a given point in a 3D space. The normal vector field is indicative of a surface orientation of the 3D surface and includes a predicted normal vector to the surface point closest to the given point. The system further determines the 3D surface representation based at least in part on the unsigned distance field and the normal vector field.

Abstract: A method may include obtaining a dataset including a target Action Unit (AU) combination and labeled images of the target AU combination with at least a first category of intensity for each AU of the target AU combination and a second category of intensity for each AU of the target AU combination. The method may also include determining that the first category of intensity for a first AU has a higher number of labeled images than the second category of intensity for the first AU, and based on the determination, identifying a number of new images to be synthesized in the second category of intensity for the first AU. The method may additionally include synthesizing the number of new images with the second category of intensity for the first AU, and adding the new images to the dataset.

Abstract: A method may include obtaining a base facial image, and obtaining a first set of base facial features within the base facial image, the first set of base facial features associated with a first facial AU to be detected in an analysis facial image. The method may also include obtaining a second set of base facial features within the base facial image, the second set of facial features associated with a second facial AU to be detected. The method may include obtaining the analysis facial image, and applying a first image normalization to the analysis facial image using the first set of base facial features to facilitate prediction of a probability of the first facial AU. The method may include applying a second image normalization to the analysis facial image using the second set of base facial features to facilitate prediction of a probability of the second facial AU.

Abstract: A method may include obtaining a base facial image, and obtaining a first set of base facial features within the base facial image, the first set of base facial features associated with a first facial AU to be detected in an analysis facial image. The method may also include obtaining a second set of base facial features within the base facial image, the second set of facial features associated with a second facial AU to be detected. The method may include obtaining the analysis facial image, and applying a first image normalization to the analysis facial image using the first set of base facial features to facilitate prediction of a probability of the first facial AU. The method may include applying a second image normalization to the analysis facial image using the second set of base facial features to facilitate prediction of a probability of the second facial AU.