US 11,869,194 B2
Image processing method and apparatus, computer-readable storage medium
Xiaoguang Gu, Shenzhen (CN); and Libo Fu, Shenzhen (CN)
Assigned to Tencent Technology (Shenzhen) Company Limited, Shenzhen (CN)
Filed by TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED, Shenzhen (CN)
Filed on Jul. 29, 2021, as Appl. No. 17/388,313.
Application 17/388,313 is a continuation of application No. PCT/CN2020/085732, filed on Apr. 20, 2020.
Claims priority of application No. 201910373797.2 (CN), filed on May 7, 2019.
Prior Publication US 2021/0366127 A1, Nov. 25, 2021
Int. Cl. G06T 7/11 (2017.01); G06T 7/187 (2017.01); G06T 7/12 (2017.01); G06T 7/194 (2017.01); G06T 7/73 (2017.01); H04N 5/272 (2006.01); G06V 40/10 (2022.01); G06F 18/23 (2023.01)
CPC G06T 7/11 (2017.01) [G06F 18/23 (2023.01); G06T 7/12 (2017.01); G06T 7/187 (2017.01); G06T 7/194 (2017.01); G06T 7/75 (2017.01); G06V 40/10 (2022.01); H04N 5/272 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/30196 (2013.01)] 14 Claims
OG exemplary drawing
 
1. An image processing method, comprising:
obtaining a to-be-processed image;
performing image semantic segmentation on the to-be-processed image to obtain a semantically-segmented image, the semantically-segmented image comprising a target region and a non-target region obtained through the semantic segmentation;
inputting the to-be-processed image into a pose recognition model;
partitioning an image region in which the target in the to-be-processed image is located using a first hidden layer of the pose recognition model;
determining first skeletal key points corresponding to the target in the image region using a second hidden layer of the pose recognition model, the first hidden layer being located in front of the second hidden layer;
determining a plurality of skeletal key points in the to-be-processed image using the first hidden layer of the pose recognition model;
clustering the plurality of skeletal key points according to targets in the to-be-processed image using the second hidden layer of the pose recognition model, to obtain second skeletal key points corresponding to the target;
outputting, using the pose recognition model, a pose-recognized image recognizing skeletal region, the skeletal region being predicted according to the first skeletal key points and the second skeletal key points;
fusing the target region and the non-target region of the semantically-segmented image with the skeletal region of the pose-recognized image, to obtain a trimap comprising foreground region, background region, and recognition region; and
generating, according to the to-be-processed image and the trimap, a transparency mask image for image separation from the to-be-processed image.