Real-time synthetically generated video from still frames

• US 10,382,799 B1
• Filed: 10/29/2018
• Issued: 08/13/2019
• Est. Priority Date: 07/06/2018
• Status: Active Grant

• Current Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Original Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Inventors: Walters, Austin, Pham, Vincent, Key, Kate, Taylor, Kenneth, Truong, Anh, Abdi Taghi Abad, Fardin, Goodsitt, Jeremy
• Primary Examiner(s): Wills, Diane M
• Application Number:
  US16/173,374
• Time in Patent Office: 288 Days
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Abstract

Systems and methods for generating synthetic video are disclosed. For example, a system may include a memory unit and a processor configured to execute the instructions to perform operations. The operations may include receiving video data, normalizing image frames, generating difference images, and generating an image sequence generator model. The operations may include training an autoencoder model using difference images, the autoencoder comprising an encoder model and a decoder model. The operations may include identifying a seed image frame and generating a seed difference image from the seed image frame. The operations may include generating, by the image sequence generator model, synthetic difference images based on the seed difference image. In some aspects, the operations may include using the decoder model to synthetic normalized image frames from the synthetic difference images. The operations may include generating synthetic video by adding background to the synthetic normalized image frames.

Claims

1. A system for generating synthetic video, the system comprising:

one or more memory units for storing instructions; and

one or more processors configured to execute the instructions to perform operations comprising;

receiving video data comprising a sequence of image frames;

normalizing the sequence of image frames;

generating a sequence of difference images based on the normalized sequence of image frames;

generating an image sequence generator model based on the sequence of difference images;

training an autoencoder model using the sequence of difference images, the autoencoder model comprising an encoder model and a decoder model;

identifying a seed image frame;

generating a seed difference image based on the seed image frame by using the encoder model to perform a forward-step encoding on the seed image frame;

generating a sequence of synthetic difference images based on the seed difference image, wherein the sequence of synthetic difference images is generated by iteratively using the image sequence generator model to accept a previous synthetic difference image in the sequence as an input and return a subsequent synthetic difference image as an output, starting from the seed difference image;

generating a sequence of synthetic normalized image frames based on the sequence of synthetic difference images, by using the decoder model to perform at least one of forward-step or backward-step decoding; and

generating synthetic video by adding background to the synthetic normalized image frames.

2. The system of claim 1, wherein:

the received video data is received from a remote device via an interface; and

the synthetic video is returned to the remote device via the interface.

3. The system of claim 1, wherein normalizing image frames comprises at least one of an image registration, a rotation, a crop, a zoom, or a parameter adjustment.

4. The system of claim 1, wherein normalizing image frames comprises background subtraction using object recognition.

5. The system of claim 1, wherein the object recognition comprises using a trained machine learning model to identify the boundary of a person in a normalized image frame.

6. The system of claim 1, wherein generating the sequence of difference images comprises pixel subtraction.

7. The system of claim 1, wherein the image sequence generator model is one of a recurrent neural network model (RNN), a generative adversarial network model (GAN), an RNN-GAN model, a convolutional neural network (CNN) model or a long short term memory model.

8. The system of claim 1, the operations further comprising storing the image sequence generator model in a model storage.

9. The system of claim 1, wherein the encoder model and the decoder model are neural network models.

10. The system of claim 9, the operations further comprising storing the autoencoder model in a model storage.

11. The system of claim 1, wherein identifying a seed image frame comprises selecting one of the normalized image frames.

12. The system of claim 1, wherein identifying a seed image frame comprises generating the seed image frame using a neural network model trained on the normalized image frames.

13. The system of claim 12, wherein the neural network model is a GAN model or a CNN model.

14. The system of claim 12, the operations further comprising retrieving, from a model storage, the image sequence generator model.

15. The system of claim 1, wherein adding background comprises combining the synthetic normalized image frames with a photo.

16. The system of claim 1, wherein adding background comprises generating a background image using a neural network model and combining the background image with a synthetic normalized image frame.

17. The system of claim 1, the operations further comprising transmitting the synthetic video to a remote device via an interface.

18. The system of claim 1, the operations further comprising storing the synthetic video in a database.

19. The system of claim 1, wherein generating the seed difference image comprises retrieving, from a model storage, the encoder model.

20. A method for generating synthetic video, the method comprising:

receiving video data comprising a sequence of image frames;

normalizing the sequence of image frames;

generating a sequence of difference images based on the normalized sequence of image frames;

generating an image sequence generator model based on the sequence of difference images;

training an autoencoder model using the sequence of difference images, the autoencoder model comprising an encoder model and a decoder model;

identifying a seed image frame;

generating a seed difference image based on the seed image frame by using the encoder model to perform a forward-step encoding on the seed image frame;

generating a sequence of synthetic difference images based on the seed difference image, wherein the sequence of synthetic difference images is generated by iteratively using the image sequence generator model to accept a previous synthetic difference image in the sequence as an input and return a subsequent synthetic difference image as an output, starting from the seed difference image;

generating a sequence of synthetic normalized image frames based on the sequence of synthetic difference images, by using the decoder model to perform at least one of forward-step or backward-step decoding; and

generating synthetic video by adding background to the synthetic normalized image frames.

21. A system for generating synthetic video, the system comprising:

one or more memory units for storing instructions; and

one or more processors configured to execute the instructions to perform operations comprising;

receiving video data comprising a sequence of image frames;

normalizing the sequence of image frames;

generating a sequence of difference images based on the normalized sequence of image frames;

generating an image sequence generator model based on the sequence of difference images;

training an autoencoder model using the sequence of difference images, the autoencoder model comprising an encoder model and a decoder model;

generating a seed difference image using a neural network model;

generating a sequence of synthetic difference images based on the seed difference image, wherein the sequence of synthetic difference images is generated by iteratively using the image sequence generator model to accept a previous synthetic difference image in the sequence as an input and return a subsequent synthetic difference image as an output, starting from the seed difference image;

generating a sequence of synthetic normalized image frames based on the sequence of synthetic difference images, by using the decoder model to perform at least one of forward-step or backward-step decoding; and

generating synthetic video by adding background to the synthetic normalized image frames.