Abstract: The present invention sets forth techniques for generating a facial animation. The techniques include receiving a latent identity code including a first set of features describing a neutral facial depiction associated with an identity and receiving a latent expression code including a second set of features describing a facial expression associated with the identity. The techniques also include generating, via a first machine learning model, an identity-specific facial representation based on a canonical facial representation and the latent identity code and generating, via a second machine learning model and based on the latent identity code, the latent expression code, and the identity-specific facial representation, a muscle actuation field tensor and one or more bone transformations associated with the deformed canonical facial representation.

Abstract: One embodiment of the present invention sets forth a technique for generating actuation values based on a target shape such that the actuation values cause a simulator to output a simulated soft body that matches the target shape. The technique includes inputting a latent code that represents a target shape and a point on a geometric mesh into a first machine learning model. The technique further includes generating, via execution of the first machine learning model, one or more simulator control values that specify a deformation of the geometric mesh, where each of the simulator control values is based on the latent code and corresponds to the input point, and generating, via execution of the simulator, a simulated soft body based on the one or more simulator control values and the geometric mesh. The technique further includes causing the simulated soft body to be outputted to a computing device.

Abstract: One embodiment of the present invention sets forth a technique for retargeting a facial expression to a different facial identity. The technique includes generating, based on an input target facial identity, a facial identity code in an input identity latent space. The technique further includes converting a spatial input point from an input facial identity space of the input target facial identity to a canonical-space point in a canonical space. The technique still further includes generating one or more canonical simulator control values based on the facial identity code, an input source facial expression, and the canonical-space point. The technique still further includes generating a simulated active soft body based on one or more identity-specific control values, wherein each identity-specific control value corresponds to one or more of the canonical simulator control values and is in an output facial identity space associated with an output target facial identity.

Abstract: One embodiment of the present invention sets forth a technique for generating actuation values based on a target shape such that the actuation values cause a simulator to output a simulated soft body that matches the target shape. The technique includes inputting a latent code that represents a target shape and a point on a geometric mesh into a first machine learning model. The technique further includes generating, via execution of the first machine learning model, one or more simulator control values that specify a deformation of the geometric mesh, where each of the simulator control values is based on the latent code and corresponds to the input point, and generating, via execution of the simulator, a simulated soft body based on the one or more simulator control values and the geometric mesh. The technique further includes causing the simulated soft body to be outputted to a computing device.

Abstract: In one aspect of the present application, a method for generating image of orthodontic treatment outcome using artificial neural network is provided, the method comprises: obtaining a picture of a patient's face with teeth exposed before an orthodontic treatment; extracting a mouth mask and a first set of tooth contour features from the picture of the patient's face with teeth exposed before the orthodontic treatment using a trained feature extraction deep neural network; obtaining a first 3D digital model representing an initial tooth arrangement of the patient and a second 3D digital model representing a target tooth arrangement of the patient; obtaining a first pose of the first 3D digital model based on the first set of tooth contour features and the first 3D digital model; obtaining a second set of tooth contour features based on the second 3D digital model at the first pose; and generating an image of the patient's face with teeth exposed after the orthodontic treatment using a trained deep neural netwo