Patents by Inventor Dimitar Petkov Dinev

Dimitar Petkov Dinev has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240354997
    Abstract: Generating images includes generating encoded data by encoding input data into a latent space. The encoded data is decoded through a first decoder having first decoder layers by processing the encoded data through one or more of the first decoder layers. The encoded data is decoded through a second decoder having second decoder layers by processing the encoded data through one or more of the second decoder layers. An updated feature map is generated by replacing at least a portion of a feature map output from a selected layer of the first decoder layers with at least a portion of a feature map output from a selected layer of the second decoder layers. An image is generated by further decoding the updated feature map through one or more additional layers of the first decoder layers.
    Type: Application
    Filed: February 16, 2024
    Publication date: October 24, 2024
    Inventors: Dimitar Petkov Dinev, Siddarth Ravichandran, Hyun Jae Kang, Ondrej Texler, Anthony Sylvain Jean-Yves Liot, Sajid Sadi
  • Publication number: 20240221260
    Abstract: Synthesizing speech and movement of a virtual human includes capturing supplemental data generated by a transducer. The supplemental data specifies one or more attributes of a user. The capturing is performed in substantially real-time with the user providing input to a conversational platform. A behavior determiner generates behavioral data based on the supplemental data and an audio response generated by the conversational platform in response to the input to the conversation platform. Based on the behavioral data and the audio response, a rendering network generates a video rendering of a virtual human engaging in a conversation with the user, the video rendering synchronized with the audio response.
    Type: Application
    Filed: June 27, 2023
    Publication date: July 4, 2024
    Inventors: Dimitar Petkov Dinev, Ondrej Texler, Siddarth Ravichandran, Janvi Chetan Palan, Hyun Jae Kang, Ankur Gupta, Anil Unnikrishnan, Anthony Sylvain Jean-Yves Liot, Sajid Sadi
  • Publication number: 20240221254
    Abstract: Image-to-image translations using 1D inputs includes concatenating multiple 1D vectors forming a concatenated 1D vector. The multiplicity of 1D vectors includes 1D vectors of at least two different modalities. An encoded 1D vector is generated by encoding the concatenated 1D vector. An encoded 2D array of features is generated by reshaping an arrangement of features of the encoded 1D feature vector. An image of a virtual human is generated by decoding the encoded 2D array.
    Type: Application
    Filed: June 27, 2023
    Publication date: July 4, 2024
    Inventors: Hyun Jae Kang, Siddarth Ravichandran, Ondrej Texler, Dimitar Petkov Dinev, Anthony Sylvain Jean-Yves Liot, Sajid Sadi
  • Publication number: 20240013464
    Abstract: Multimodal disentanglement can include generating a set of silhouette images corresponding to a human face, the generating undoing a correlation between an upper portion and a lower portion of the human face depicted by each silhouette image. A unimodal machine learning model can be trained with the set of silhouette images. As trained, the unimodal machine learning model can generate synthetic images of the human face. The synthetic images generated by the unimodal machine learning model once trained can be used to train a multimodal rendering network. The multimodal rendering network can be trained to generate a voice-animated digital human. Training the multimodal rendering network can be based on minimizing differences between the synthetic images and images generated by the multimodal rendering network.
    Type: Application
    Filed: April 5, 2023
    Publication date: January 11, 2024
    Inventors: Siddarth Ravichandran, Dimitar Petkov Dinev, Ondrej Texler, Ankur Gupta, Janvi Chetan Palan, Hyun Jae Kang, Anthony Sylvain Jean-Yves Liot, Sajid Sadi
  • Publication number: 20230394732
    Abstract: Creating images and animations of lip motion from mouth shape data includes providing, as one or more input features to a neural network model, a vector of a plurality of coefficients. Each vector of the plurality of coefficients corresponds to a different mouth shape. Using the neural network model, a data structure output specifying a visual representation of a mouth including lips having a shape corresponding to the vector is generated.
    Type: Application
    Filed: October 17, 2022
    Publication date: December 7, 2023
    Inventors: Siddarth Ravichandran, Anthony Sylvain Jean-Yves Liot, Dimitar Petkov Dinev, Ondrej Texler, Hyun Jae Kang, Janvi Chetan Palan, Sajid Sadi
  • Publication number: 20230394715
    Abstract: Image generation using a hierarchical, model-based system includes generating a first region of an image using a first neural network model. The first region of the image is provided to a second neural network model as input. A second region of the image is generated using the second neural network model. The second region of the image shares a boundary with at least a portion of the first region of the image.
    Type: Application
    Filed: October 17, 2022
    Publication date: December 7, 2023
    Inventors: Ondrej Texler, Dimitar Petkov Dinev, Ankur Gupta, Hyun Jae Kang, Anthony Sylvain Jean-Yves Liot, Siddarth Ravichandran, Sajid Sadi