True Meeting Inc.

Omri Kaduri

Ran Oz

Yuval Gronau

Osnat Goren-Peyser

Yuval Spiegel

A method for virtually mimicking an arm movement of participant of a three dimensional (3D) video conference, the method may include (i) obtaining joints movement information about joints movements of an arm of a participant of the 3D video conference, based on a movement of the arm of the participant that was captured by a video taken by a camera of the participant, wherein arm of the participant is free of physical joint movement markers; (ii) generating, an arm skeletal model that represents the joins movement of the arm, based on the joints movement information; (iii) generating, by a machine learning process, a renderable 3D model of the arm that mimics the movement of the arm of the participant; and (iv) responding to the generating of the renderable 3D model, wherein the responding comprises at least one out of (a) rendering an avatar of the participant based on the renderable 3D model, (b) storing renderable 3D model information, or (c) sending the renderable 3D model information to another computerized device related to the 3D video conference.

Arm movement mimicking

A method for virtual 3D video conference environment generation, the method may include (a) determining a first optical axis of a first virtual camera, the first optical axis represents a line of sight of the participant while a participant of the 3D video conference environment looks at a current displayed version of a virtual 3D video conference environment (V3DVCE); the current displayed version of the V3DVCE is displayed on a display; (b) determining a second optical axis of a second virtual camera that virtually captures the V3DVCE to provide the current displayed version of the V3DVCE; and (c) generating a next displayed version of the V3DVCE based on at least one of the first optical axis and the second optical axis.

Virtual 3D video conference environment generation

A method for three dimensional modeling and rendering of head hair, the method may include obtaining a three dimensional (3D) point cloud of head hair points of a person; determining head hair points related 3D directions indicative of 3D directions of candidate strands that pass through the head hair points; grouping the hear hair points into multiple strand groups, wherein the grouping is based on distances between the head hair points and the head hair points 3D directions; wherein each strand group is associated with strand spatial information; and generating, by a machine learning process, a representation of the head hair based on strand spatial information of the multiple strand groups and on strand shape information.

Growing community of inventors

Los Altos, CA, United States of America

Omri Kaduri