Real time video processing for changing proportions of an object in the video

• US 10,586,570 B2
• Filed: 06/25/2014
• Issued: 03/10/2020
• Est. Priority Date: 02/05/2014
• Status: Active Grant

• Current Assignee: Snap, Inc.
• Original Assignee: Snap, Inc.
• Inventors: Shaburova, Elena
• Primary Examiner(s): Niu, Feng
• Application Number:
  US14/314,312
• Publication Number:
  US 20150221118A1
• Time in Patent Office: 2,085 Days
• Field of Search: None
• CPC Class Codes: G06F 3/011 Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06T 11/001 Texturing; Colouring; Generation of texture or colour inpainting G06T5/77
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 13/40 of characters, e.g. humans, animals or virtual beings
• CPC Class Codes: G06T 17/20 Finite element generation, e.g. wire-frame surface description, tesselation
• CPC Class Codes: G06T 19/20 Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10021 Stereoscopic video; Stereoscopic image sequence
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/20036 Morphological image processing
• CPC Class Codes: G06T 2207/30201 Face
• CPC Class Codes: G06T 5/77 Retouching; Inpainting; Scratch removal
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/251 involving models
• CPC Class Codes: G06T 7/33 using feature-based methods
• CPC Class Codes: G06T 7/90 Determination of colour characteristics
• CPC Class Codes: G06V 10/40 Extraction of image or video features
• CPC Class Codes: G06V 10/42 Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
• CPC Class Codes: G06V 10/467 Encoded features or binary features, e.g. local binary patterns [LBP]
• CPC Class Codes: G06V 10/56 : relating to colour
• CPC Class Codes: G06V 10/60 : relating to illumination properties, e.g. using a reflectance or lighting model
• CPC Class Codes: G06V 20/40 : in video content extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44
• CPC Class Codes: G06V 20/46 : Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
• CPC Class Codes: G06V 40/167 : using comparisons between temporally consecutive images
• CPC Class Codes: G06V 40/168 : Feature extraction; Face representation
• CPC Class Codes: G06V 40/171 : Local features and components; Facial parts eye characteristics G06V40/18; Occluding parts, e.g. glasses; Geometrical relationships
• CPC Class Codes: G06V 40/174 : Facial expression recognition
• CPC Class Codes: G06V 40/20 : Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: G11B 27/031 : Electronic editing of digitised analogue information signals, e.g. audio or video signals
• CPC Class Codes: H04N 5/262 : Studio circuits, e.g. for mixing, switching-over, change of character of image, other special effects ; Cameras specially adapted for the electronic generation of special effects
• CPC Class Codes: H04N 9/79 : Processing of colour television signals in connection with recording

Abstract

Methods and systems are presented for transforming frames of a video. The method provides an object in the video that at least partially and at least occasionally is presented in frames of a video. An object is detected in the video. Detection of the object comprises detecting feature reference points of the object. The object is tracked in the video. The tracking comprises creating a mesh that is based on the detected feature reference points of the object and aligning the mesh to the object in each frame. A first set of node points is generated on the mesh based on a request for changing proportions. A second set of nodes points is generated based on the first set of node points. The frames of the video are transformed in such way that the object'"'"'s proportions are transformed in accordance with the second set of the node points using the mesh.

Claims

1. A computer implemented method for real time video processing for changing proportions of an object in a video, the method comprising:

receiving a video stream depicting a face that at least partially and at least occasionally is presented in frames of the video stream;

receiving a user request for changing a proportion of the face depicted in the video stream;

after receiving the user request for changing the proportion of the face, detecting the face in the video stream by detecting feature reference points of the face;

tracking the detected face in the video stream, wherein the tracking comprises creating a first mesh based on the detected feature reference points of the face and aligning the first mesh to the face in each frame;

while tracking the detected face with the first mesh, transforming a set of pixels within the frames of the video stream representing a portion of the feature reference points to generate transformed frames of the video stream, wherein the transforming comprises deforming a first portion of the face depicted in the video stream by a first deformation amount, in accordance with the user request for changing the proportion, while deforming a second portion of the face depicted in the video stream by a second deformation amount;

maintaining the first mesh while the face is present in the frames of the video stream; and

providing the transformed frames within the video.

2. The computer implemented method of claim 1, further comprising:

associating a square grid with a background of the object in the video; and

transforming the background of the object using the square grid to avoid the background distortion.

3. The computer implemented method of claim 1, wherein the feature reference points are at least one of points indicating eyebrows vertical position, eyes vertical position, eyes width, eyes height, eye separation distance, nose vertical position, nose pointing up, mouth vertical position, mouth width, chin width, upper lip raiser, jaw drop, lip stretcher, left brow lowerer, right brow lowerer, lip corner depressor, or outer brow raiser.

4. The computer implemented method of claim 1, wherein the method further comprises:

indicating a presence of an object from a list of objects in frames of the video, wherein the list further comprises rules for changing proportions of each object from the list; and

generating a request for changing proportions of the object which presence in frames of the video is indicated.

5. The computer implemented method of claim 1, wherein the method further comprises:

defining an object to be changed in frames of the video and rules for changing proportions of the object by a user; and

generating a request for changing proportions of the object defined by the user.

6. The computer implemented method of claim 5, wherein the method further comprises:

defining by a user a frame area of the video to be processed, wherein the frame area to be processed sets a frame area of the video where only proportions of those objects or their parts which are positioned in the frame area to be processed are changed.

7. The computer implemented method of claim 1, wherein the method further comprises:

randomly selecting at least one object to be changed in frames of the video out of objects in the frames of the video and randomly selecting at least one rule for changing proportions of the selected object out of a list of rules; and

generating a request for changing proportions of the randomly selected object based on the randomly selected rules.

8. The computer implemented method of claim 1 further comprising:

applying a transformation function only in vertices of a square grid associated with the frames; and

after the transformation function is applied only in the vertices, computing values in a collection of pixels approximately using linear interpolation in a first direction based on values of the vertices; and

after computing the values in the collection of pixels in the first direction, computing values of the collection of pixels approximately using linear interpolation in a second direction based on the values of the vertices.

9. The computer implemented method of claim 1, wherein the detecting is implemented with the use of an Active Shape Model (ASM) or Viola Jones method.

10. A mobile computerized system comprising a central processing unit and a memory, the memory storing instructions for:

receiving a video stream depicting a face that at least partially and at least occasionally is presented in frames of the video stream;

receiving a user request for changing a proportion of the face depicted in the video stream;

after receiving the user request for changing the proportion of the face, detecting the face in the video stream by detecting feature reference points of the face;

tracking the detected face in the video stream, wherein the tracking comprises creating a first mesh based on the detected feature reference points of the face and aligning the first mesh to the face in each frame;

while tracking the detected face with the first mesh, transforming a set of pixels within the frames of the video stream representing a portion of the feature reference points to generate transformed frames of the video stream, wherein the transforming comprises deforming a first portion of the face depicted in the video stream by a first deformation amount, in accordance with the user request for changing the proportion, while deforming a second portion of the face depicted in the video stream by a second deformation amount;

maintaining the first mesh while the face is present in the frames of the video stream; and

providing the transformed frames.

11. The mobile computerized system of claim 10, wherein the memory further stores instructions for:

associating a square grid with a background of the face in the video stream; and

transforming the background of the face using the square grid to avoid the background distortion.

12. The mobile computerized system of claim 10, wherein the feature reference points are at least one of points indicating eyebrows vertical position, eyes vertical position, eyes width, eyes height, eye separation distance, nose vertical position, nose pointing up, mouth vertical position, mouth width, chin width, upper lip raiser, jaw drop, lip stretcher, left brow lowerer, right brow lowerer, lip corner depressor, or outer brow raiser.

13. The mobile computerized system of claim 10, wherein the memory storing further instructions for:

indicating a presence of an object from a list of objects in the frames of the video stream, wherein the list further comprises rules for changing proportions of each object from the list; and

generating a request for changing proportions of the object which presence in frames of the video is indicated.

14. The mobile computerized system of claim 10, wherein the memory storing further instructions for:

defining an object to be changed in the frames of the video stream and rules for changing proportions of the object by a user; and

generating a request for changing proportions of the object defined by the user.

15. The mobile computerized system of claim 14, wherein the memory storing further instructions for:

defining by a user a frame area of the video stream to be processed, wherein the frame area to be processed sets a frame area of the video stream where only proportions of those objects or their parts which are positioned in the frame area to be processed are changed.

16. The mobile computerized system of claim 10, wherein the memory storing further instructions for:

randomly selecting at least one object to be changed in the frames of the video stream out of the objects in the frames of the video stream and randomly selecting at least one rule for changing proportions of the selected object out of a list of rules; and

generating a request for changing proportions of the randomly selected object based on the randomly selected rules.

17. The mobile computerized system of claim 10, wherein the memory storing further instructions for:

applying a transformation function only in vertices of a square grid associated with the frames; and

after the transformation function is applied only in the vertices, computing values in a collection of pixels approximately using linear interpolation in a first direction based on values of the vertices; and

after computing the values in the collection of pixels in the first direction, computing values of the collection of pixels approximately using linear interpolation in a second direction based on the values of the vertices.

18. The mobile computerized system of claim 10, wherein the detecting is implemented with the use of an Active Shape Model (ASM) or Viola Jones method.