Bodygram, Inc.

Visualize K.k.

Subas Chhatkuli

Chong Jin Koh

Kyohei Kamiyama

Bryan Atwood

Jin Koh

A method includes receiving from a camera a 2D front-view digital image (FDI) and side-view digital image (SDI) of a human body; receiving one or more camera parameters (CPs); executing a front depth-estimation model (DEM) generated through pre-training a first machine-learning model (MLM) to convert the FDI and SDI into a front depth map (DM) and side DM, respectively; executing a rear DEM generated through pre-training a second MLM to estimate a rear DM based on the FDI, SDI, and front and side DMs; combining the front and rear DMs to generate a 3D model; executing a front key-point (FKP) estimation model generated through pre-training a third MLM to estimate one or more FKPs; and extracting a body measurement based on the 3D model and FKPs, the first and second MLMs each including a respective encoder and decoder, each respective decoder configured to be executed based on the CPs.

Self-implementing accurate body measurements by a user using a mobile device

Systems and methods for generating a prediction of a body composition of a user using an image capturing device are disclosed. The systems and methods can be used to predict body compositions such as body fat percentage, water content percentage, muscle mass, bone mass, and so on, from a single user image. The methods include the steps of receiving one or more user images and one or more user parameters, generating one or more key points based on the one or more user images, and generating a prediction of the body composition of the user based on the one or more key points and the one or more user parameters, using a body composition deep learning network (DLN). In one embodiment, the body composition DLN comprises a face image DLN, a body feature DLN, and an output DLN.

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Tokyo, Japan

Subas Chhatkuli