Deepbrain Ai Inc.

Guem Buel Hwang

Gyeong Su Chae

An apparatus for generating a speech synthesis image according to a disclosed embodiment is an apparatus for generating a speech synthesis image based on machine learning, the apparatus including a first global geometric transformation predictor configured to be trained to receive each of a source image and a target image including the same person, and predict a global geometric transformation for a global motion of the person between the source image and the target image based on the source image and the target image, a local feature tensor predictor configured to be trained to predict a feature tensor for a local motion of the person based on input target image-related information, and an image generator configured to be trained to reconstruct the target image based on the global geometric transformation, the source image, and the feature tensor for the local motion.

Apparatus and method for generating speech synthesis image

An apparatus for generating a speech synthesis image according to a disclosed embodiment is an apparatus for generating a speech synthesis image based on machine learning, the apparatus including a first global geometric transformation predictor configured to be trained to receive each of a source image and a target image including the same person, and predict a global geometric transformation for a global motion of the person between the source image and the target image based on the source image and the target image, a local feature tensor predictor configured to be trained to predict a feature tensor for a local motion of the person based on preset input data, and an image generator configured to be trained to reconstruct the target image based on the global geometric transformation, the source image, and the feature tensor for the local motion.

An apparatus for generating a lip sync image according to disclosed embodiment has one or more processors and a memory which stores one or more programs executed by the one or more processors. The apparatus includes a first artificial neural network model configured to generate an utterance match synthesis image by using a person background image and an utterance match audio signal corresponding to the person background image as an input, and generate an utterance mismatch synthesis image by using the person background image and an utterance mismatch audio signal not corresponding to the person background image as an input, and a second artificial neural network model configured to output classification values for an input pair in which an image and a voice match and an input pair in which an image and a voice do not match by using the input pairs as an input.

Apparatus and method for generating lip sync image

An image synthesis device according to a disclosed embodiment has one or more processors and a memory which stores one or more programs executed by the one or more processors. The image synthesis device includes a first artificial neural network provided to learn each of a first task of using a damaged image as an input to output a restored image and a second task of using an original image as an input to output a reconstructed image, and a second artificial neural network connected to an output layer of the first artificial neural network, and trained to use the reconstructed image output from the first artificial neural network as an input and improve the image quality of the reconstructed image.

Device and method for synthesizing image capable of improving image quality

An apparatus for generating a lip sync image according to a disclosed embodiment has one or more processors and a memory which stores one or more programs executed by the one or more processors. The apparatus includes a first artificial neural network model configured to generate an utterance synthesis image by using a person background image and an utterance audio signal corresponding to the person background image as an input, and generate a silence synthesis image by using only the person background image as an input, and a second artificial neural network model configured to output, from a preset utterance maintenance image and the first artificial neural network model, classification values for the preset utterance maintenance image and the silence synthesis image by using the silence synthesis image as an input.

An image synthesis device according to a disclosed embodiment is an image synthesis device has one or more processors and a memory which stores one or more programs executed by the one or more processors. The image synthesis device includes a first artificial neural network model provided to learn each of a first task of using a damaged image as an input to output a restored image and a second task of using an original image as an input to output a reconstructed image, and a second artificial neural network model trained to use the reconstructed image output from the first artificial neural network model as an input and improve the image quality of the reconstructed image.

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Seoul, South Korea

Guem Buel Hwang