Systems and methods are disclosed for creating a machine generated avatar. A machine generated avatar is an avatar generated by processing video and audio information extracted from a recording of a human speaking a reading corpora and enabling the created avatar to be able to say an unlimited number of utterances, i.e., utterances that were not recorded. The video and audio processing consists of the use of machine learning algorithms that may create predictive models based upon pixel, semantic, phonetic, intonation, and wavelets.

A method for outputting a blend shape value includes: performing feature extraction on obtained target audio data to obtain a target audio feature vector; inputting the target audio feature vector and a target identifier into an audio-driven animation model; inputting the target audio feature vector into an audio encoding layer, determining an input feature vector of a next layer at a (2t−n)/2 time point based on an input feature vector of a previous layer between a t time point and a t-n time point, determining a feature vector having a causal relationship with the input feature vector of the previous layer as a valid feature vector, outputting sequentially target-audio encoding features, and inputting the target identifier into a one-hot encoding layer for binary vector encoding to obtain a target-identifier encoding feature; and outputting a blend shape value corresponding to the target audio data.

A computing device according to an embodiment includes one or more processors, a memory storing one or more programs executed by the one or more processors, a standby state image generating module configured to generate a standby state image in which a person is in a standby state, and generate a back-motion image set including a plurality of back-motion images at a preset frame interval from the standby state image for image interpolation between a preset reference frame of the standby state image, a speech state image generating module configured to generate a speech state image in which a person is in a speech state based on a source of speech content, and an image playback module configured to generate a synthetic speech image by combining the standby state image and the speech state image while playing the standby state image.

A device according to an embodiment has one or more processors and a memory storing one or more programs executable by the one or more processors. The device includes a first encoder configured to receive a person background image corresponding to a video part of a speech video of a person and extract an image feature vector from the person background image, a second encoder configured to receive a speech audio signal corresponding to an audio part of the speech video and extract a voice feature vector from the speech audio signal, a combiner configured to generate a combined vector by combining the image feature vector output from the first encoder and the voice feature vector output from the second encoder, and a decoder configured to reconstruct the speech video of the person using the combined vector as an input.

A device for generating a speech moving image according to an embodiment includes a first encoder that receives a person background image in which a portion related to speech of a person that is a video part of the speech moving image of the person is covered with a mask, extracts an image feature vector from the person background image, and compresses the extracted image feature vector, a second encoder that receives a speech audio signal that is an audio part of the speech moving image, extracts a voice feature vector from the speech audio signal, and compresses the extracted voice feature vector, a combination unit that generates a combination vector of the compressed image feature vector and the compressed voice feature vector, and an image reconstruction unit that reconstructs the speech moving image of the person with the combination as an input.

A virtual object lip driving method performed by an electronic device includes: obtaining a speech segment and target face image data about a virtual object; and inputting the speech segment and the target face image data into a first target model to perform a first lip driving operation, so as to obtain first lip image data about the virtual object driven by the speech segment. The first target model is trained in accordance with a first model and a second model, the first model is a lip-speech synchronization discriminative model with respect to lip image data, and the second model is a lip-speech synchronization discriminative model with respect to a lip region in the lip image data.

Eyewear having a speech to moving lips algorithm that receives and translates speech and utterances of a person viewed through the eyewear, and then displays an overlay of moving lips corresponding to the speech and utterances on a mask of the viewed person. A database having text to moving lips information is utilized to translate the speech and generate the moving lips in near-real time with little latency. This translation provides the deaf/hearing impaired users the ability to understand and communicate with the person viewed through the eyewear when they are wearing a mask. The translation may include automatic speech recognition (ASR) and natural language understanding (NLU) as a sound recognition engine.

A device for generating a speech video may include a first encoder to receive a person background image corresponding to a video part of a speech video of a person and extract an image feature vector from the person background image, a second encoder to receive a speech audio signal corresponding to an audio part of the speech video and extract a voice feature vector from the speech audio signal, a combiner to generate a combined vector by combining the image feature vector output from the first encoder and the voice feature vector output from the second encoder, and a decoder to reconstruct the speech video of the person using the combined vector as an input. The person background image input to the first encoder includes a face and an upper body of the person, with a portion related to speech of the person covered with a mask.

A system, method and apparatus for detecting facial expressions according to EMG signals.

Apparatuses, systems, and techniques are presented to generate media content. In at least one embodiment, a first neural network is used to generate first video information based, at least in part, upon voice information corresponding to one or more users, and a second neural network is used to generate second video information corresponding to the one or more users based, at least in part, upon the first video information and one or more images corresponding to the one or more users