Abstract: Systems and methods are disclosed that animate a source portrait image with motion (i.e., pose and expression) from a target image. In contrast to conventional systems, given an unseen single-view portrait image, an implicit three-dimensional (3D) head avatar is constructed that not only captures photo-realistic details within and beyond the face region, but also is readily available for animation without requiring further optimization during inference. In an embodiment, three processing branches of a system produce three tri-planes representing coarse 3D geometry for the head avatar, detailed appearance of a source image, as well as the expression of a target image. By applying volumetric rendering to a combination of the three tri-planes, an image of the desired identity, expression and pose is generated.

Abstract: Approaches presented herein provide systems and methods for disentangling identity from expression input models. One or more machine learning systems may be trained directly from three-dimensional (3D) points to develop unique latent codes for expressions associated with different identities. These codes may then be mapped to different identities to independently model an object, such as a face, to generate a new mesh including an expression for an independent identity. A pipeline may include a set of machine learning systems to determine model parameters and also adjust input expression codes using gradient backpropagation in order train models for incorporation into a content development pipeline.

Abstract: Techniques are disclosed herein for authenticating users. The techniques include generating a first fingerprint that represents one or more motions of a first avatar that is driven by a first user, and determining an identity of the first user based on the first fingerprint and a second fingerprint associated with the first user.

Abstract: A system, method, and apparatus for generating a normalized three-dimensional model of a human face from a single unconstrained two-dimensional image of the human face. The system includes a processor that executes instructions including receiving the single unconstrained two-dimensional image of the human face, using an inference network to determine an inferred normalized three-dimensional model of the human face based on the single unconstrained two-dimensional image of the human face, and using a refinement network to iteratively determine the normalized three-dimensional model of the human face with a neutral expression and unshaded albedo textures under diffuse lighting conditions based on the inferred normalized three-dimensional model of the human face.

Abstract: A method and system for performing novel image synthesis using generative networks are provided. The encoder-based model is trained to infer a 3D representation of an input image. A feature image is then generated using volume rendering techniques in accordance with the 3D representation. The feature image is then concatenated with a noisy image and processed by a denoiser network to predict an output image from a novel viewpoint that is consistent with the input image. The denoiser network can be a modified Noise Conditional Score Network (NCSN). In some embodiments, multiple input images or keyframes can be provided as input, and a different 3D representation is generated for each input image. The feature image is then generated, during volume rendering, by sampling each of the 3D representations and applying a mean-pooling operation to generate an aggregate feature image.

Abstract: A method for generating, by an encoder-based model, a three-dimensional (3D) representation of a two-dimensional (2D) image is provided. The encoder-based model is trained to infer the 3D representation using a synthetic training data set generated by a pre-trained model. The pre-trained model is a 3D generative model that produces a 3D representation and a corresponding 2D rendering, which can be used to train a separate encoder-based model for downstream tasks like estimating a triplane representation, neural radiance field, mesh, depth map, 3D key points, or the like, given a single input image, using the pseudo ground truth 3D synthetic training data set. In a particular embodiment, the encoder-based model is trained to predict a triplane representation of the input image, which can then be rendered by a volume renderer according to pose information to generate an output image of the 3D scene from the corresponding viewpoint.

Abstract: A system, method, and apparatus for generating a normalization of a single two-dimensional image of an unconstrained human face. The system receives the single two-dimensional image of the unconstrained human face, generates an undistorted face based on the unconstrained human face by removing perspective distortion from the unconstrained human face via a perspective undistortion network, generates an evenly lit face based on the undistorted face by normalizing lighting of the undistorted face via a lighting translation network, and generates a frontalized and neutralized expression face based on the evenly lit face via an expression neutralization network.

Abstract: Techniques are disclosed herein for verifying user identities. The techniques include generating one or more features based on at least one of video data or audio data generated during a computer-mediated interaction between a plurality of users in which a first user included in the plurality of users is represented by an avatar, and verifying an identity of the first user based on the one or more features that are generated and one or more features associated with the first user.

Abstract: Apparatuses, systems, and techniques are presented to generate image data. In at least one embodiment, one or more neural networks are used to cause a lighting effect to be applied to one or more objects within one or more images based, at least in part, on synthetically generated images of the one or more objects.

Abstract: A system and method for generating a digital avatar from a two-dimensional input image in accordance with a machine learning models is provided. The machine learning models are generative adversarial networks trained to process a latent code into three-dimensional data and color data. A generative adversarial network (GAN) inversion optimization algorithm is run on the first machine learning model to map the input image to a latent code for the first machine learning model. The latent code is used to generate unstructured 3D data and color information. A GAN inversion optimization algorithm is then run on the second machine learning model to determine a latent code for the second machine learning model, based at least on the output of the first machine learning model. The latent code for the second machine learning model is then used to generate the data for the digital avatar.

Abstract: In examples, locations of facial landmarks may be applied to one or more machine learning models (MLMs) to generate output data indicating profiles corresponding to facial expressions, such as facial action coding system (FACS) values. The output data may be used to determine geometry of a model. For example, video frames depicting one or more faces may be analyzed to determine the locations. The facial landmarks may be normalized, then be applied to the MLM(s) to infer the profile(s), which may then be used to animate the mode for expression retargeting from the video. The MLM(s) may include sub-networks that each analyze a set of input data corresponding to a region of the face to determine profiles that correspond to the region. The profiles from the sub-networks, along global locations of facial landmarks may be used by a subsequent network to infer the profiles for the overall face.

Abstract: A single two-dimensional (2D) image can be used as input to obtain a three-dimensional (3D) representation of the 2D image. This is done by extracting features from the 2D image by an encoder and determining a 3D representation of the 2D image utilizing a trained 2D convolutional neural network (CNN). Volumetric rendering is then run on the 3D representation to combine features within one or more viewing directions, and the combined features are provided as input to a multilayer perceptron (MLP) that predicts and outputs color (or multi-dimensional neural features) and density values for each point within the 3D representation. As a result, single-image inverse rendering may be performed using only a single 2D image as input to create a corresponding 3D representation of the scene in the single 2D image.

Abstract: Apparatuses, systems, and techniques are presented to generate media content.

Abstract: A system, method, and apparatus for generating a normalized three-dimensional model of a human face from a single unconstrained two-dimensional image of the human face. The system includes a processor that executes instructions including receiving the single unconstrained two-dimensional image of the human face, using an inference network to determine an inferred normalized three-dimensional model of the human face based on the single unconstrained two-dimensional image of the human face, and using a refinement network to iteratively determine the normalized three-dimensional model of the human face with a neutral expression and unshaded albedo textures under diffuse lighting conditions based on the inferred normalized three-dimensional model of the human face.

Abstract: A system, method, and apparatus for generating a normalization of a single two-dimensional image of an unconstrained human face. The system receives the single two-dimensional image of the unconstrained human face, generates an undistorted face based on the unconstrained human face by removing perspective distortion from the unconstrained human face via a perspective undistortion network, generates an evenly lit face based on the undistorted face by normalizing lighting of the undistorted face via a lighting translation network, and generates a frontalized and neutralized expression face based on the evenly lit face via an expression neutralization network.

Abstract: A system and method for generating real-time facial animation is disclosed. The system relies upon pre-generating a series of key expression images from a single neutral image using a pre-trained generative adversarial neural network. The key expression images are used to generate a set of FACS expressions and associated textures which may be applied to a three-dimensional model to generate facial animation. The FACS expressions and textures may be provided to a mobile device to enable that mobile device to generate convincing three-dimensional avatars in real-time with convincing animation in a processor non-intensive way through a blending process using the pre-determined FACS expressions and textures.

Abstract: A system and method for generating real-time facial animation is disclosed. The system relies upon pre-generating a series of key expression images from a single neutral image using a pre-trained generative adversarial neural network. The key expression images are used to generate a set of FACS expressions and associated textures which may be applied to a three-dimensional model to generate facial animation. The FACS expressions and textures may be provided to a mobile device to enable that mobile device to generate convincing three-dimensional avatars in real-time with convincing animation in a processor non-intensive way through a blending process using the pre-determined FACS expressions and textures.

Abstract: A system for generating three-dimensional facial models including photorealistic hair and facial textures includes creating a facial model with reliance upon neural networks based upon a single two-dimensional input image. The photorealistic hair is created by finding a subset of similar three-dimensional polystrip hairstyles from a large database of polystrip hairstyles, selecting the most-alike polystrip hairstyle, deforming that polystrip hairstyle to better fit the hair of the two-dimensional image. Then, collisions and bald spots are corrected, and suitable textures are applied. Finally, the facial model and polystrip hairstyle are combined into a final three-dimensional avatar.

Abstract: A bacteria removing water discharge device used for a water supply facility is provided. The device includes: a bacteria removing water generating part denaturing tap water to generate bacteria removing water; a bacteria removing water discharge part, the part including a water storage part storing the bacteria removing water temporally, a water discharge port discharging the bacteria removing water stored in the water storage part onto a water receiving part of the water supply facility; and a controller, the controller executing a bacteria removing mode discharging the bacteria removing water from the bacteria removing water discharge part to the water receiving part, and a residual water drain mode draining at least a portion of the bacteria removing water remained in the water storage part after executing the bacteria removing mode.

Abstract: A water discharge apparatus WD executes a water film formation step of ejecting running water and forming a water-splash suppression water film of the running water on a surface of a sterilization object, before executing a sterilization step of discharging sterilization water from a sterilization water ejection unit 20 toward the sterilization object.