Shiseido Company, Limited

Northeastern University

Bin Sun

Yun Fu

Shuyang Wang

Songyao Jiang

Sumin Lee

Haiyi Mao

Kai Ho Edgar Cheung

Embodiments identify joints of a multi-limb body in an image. One such embodiment unifies depth of a plurality of multi-scale feature maps generated from an image of a multi-limb body to create a plurality of feature maps each having a same depth. In turn, for each of the plurality of feature maps having the same depth, an initial indication of one or more joints in the image is generated. The one or more joints are located at an interconnection of a limb to the multi-limb body or at an interconnection of a limb to another limb. To continue, a final indication of the one or more joints in the image is generated using each generated initial indication of the one or more joints.

Light-weight pose estimation network with multi-scale heatmap fusion

A system, neural network, and corresponding method generate 3D landmarks associated with an object in a 2D image. An embodiment is a system comprising a neural network detector configured to produce planar coordinates of landmarks at points of the object in the 2D image and a depth coordinate estimator. The planar coordinates include planar coordinate pairs. The depth coordinate estimator is configured to receive the 2D image and the planar coordinates and to estimate a depth coordinate for each planar coordinate pair of each landmark to generate the 3D landmarks. The system reduces network parameters from MB to KB and has better performance relative to state-of-the-art methods. The system may be configured to apply the 3D landmarks for face alignment, virtual face makeup, face recognition, eye gaze tracking, face synthesis, or other face related application.

System and method for generating image landmarks

The present disclosure provides systems and methods for virtual facial makeup simulation through virtual makeup removal and virtual makeup add-ons, virtual end effects and simulated textures. In one aspect, the present disclosure provides a method for virtually removing facial makeup, the method comprising providing a facial image of a user with makeups being applied thereto, locating facial landmarks from the facial image of the user in one or more regions, decomposing some regions into first channels which are fed to histogram matching to obtain a first image without makeup in that region and transferring other regions into color channels which are fed into histogram matching under different lighting conditions to obtain a second image without makeup in that region, and combining the images to form a resultant image with makeups removed in the facial regions. The disclosure also provides systems and methods for virtually generating output effects on an input image having a face, for creating dynamic texturing to a lip region of a facial image, for a virtual eye makeup add-on that may include multiple layers, a makeup recommendation system based on a trained neural network model, a method for providing a virtual makeup tutorial, a method for fast facial detection and landmark tracking which may also reduce lag associated with fast movement and to reduce shaking from lack of movement, a method of adjusting brightness and of calibrating a color and a method for advanced landmark location and feature detection using a Gaussian mixture model.

Systems and methods for virtual facial makeup removal and simulation, fast facial detection and landmark tracking, reduction in input video lag and shaking, and method for recommending makeup

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Boston, MA, United States of America

Bin Sun