Invention Grant
- Patent Title: Method and system of rendering a 3D image for automated facial morphing with a learned generic head model
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Application No.: US17338509Application Date: 2021-06-03
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Publication No.: US11769309B2Publication Date: 2023-09-26
- Inventor: Mathew Powers
- Applicant: Mathew Powers
- Applicant Address: US NY new york
- Assignee: Mathew Powers
- Current Assignee: Mathew Powers
- Current Assignee Address: US NY new york
- Main IPC: G06T19/20
- IPC: G06T19/20 ; G06T17/20 ; G06V40/16

Abstract:
In one aspect, a computerized method for rendering a three-dimensional (3D) digital image for automated facial morphing includes the step scanning of the user's face with a digital camera to obtain a set of digital images of the user's face. The method includes the step of determining that a user's face is in a compliant state. The method includes the step of implementing an analysis of the set of digital images and implementing a set of pre-rendering steps. Each digital image comprises a depth data, a red/green/blue (RGB) data, and a facemask data. The method then implements an iterative closest path (ICP) algorithm that correlates the set of digital images together by stitching together the cloud of points of the facemask data of each digital image and outputs a set of transformation matrices. The method includes the step of implementing a truncated signed distance function (TSDF) algorithm on the set of transformation matrices. The TSDF algorithm represents each point of the transformation matrices in a regularized voxel grid and outputs a set of voxel representations as a one-dimension (1-D) array of voxels. The method includes the step of implementing a marching cubes algorithm that obtains each voxel representation of the 1-D array of voxels and creates a three-dimensional (3D) mesh out of the per-voxel values provided by the TSDF and outputs a mesh representation. The mesh representation comprises a set of triangles and vertices. The method comprises the step of implementing a cleaning algorithm that obtains the mesh representation and cleans the floating vertices and triangles and outputs a mesh. The mesh comprises a set of scattered points with a normal per point. The method includes the step of implementing a Poisson algorithm on the mesh output and fills in any holes of the mesh. The Poisson algorithm outputs a reconstructed mesh. The method fits the reconstructed mesh on a trained three-dimensional (3D) face model and a specified machine learning algorithm is used to fit the trained 3D face model to the 3D landmarks in the reconstructed mesh.
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