Qualcomm Incorporated

Adobe, Inc.

Minghua Liu

Radomir Mech

Fatih Murat Porikli

Yinhao Zhu

Hong Cai

Minhyuk Sung

Hao Su

Systems and techniques are provided for part segmentation. For example, a process for performing part segmentation can include obtaining a three-dimensional capture of an object. The method can include generating one or more two-dimensional images of the object from the three-dimensional capture of the object. The method can further include processing the one or more two-dimensional images of the object to generate at least one two-dimensional bounding box associated with a part of the object. The method can include performing three-dimensional part segmentation of the part of the object based on a three-dimensional point cloud generated from the one or more two-dimensional images of the object and the at least one two-dimensional bounding box and based on semantically labeled super points which are merged into subgroups associated with the part of the object.

Three-dimensional object part segmentation using a machine learning model

This disclosure includes technologies for deformation of 3D shapes using meta-handles. The disclosed 3D conditional generative system takes control points with biharmonic coordinates as deformation handles for a shape to train a network to learn a set of meta-handles for the shape. Further, each deformation axis of the latent space of deformation is explicitly associated with a meta-handle from a set of disentangled meta-handles, and the disentangled meta-handles factorize plausible deformations of the shape. Advantageously, an intuitive deformation of the shape may be generated by manipulating coefficients of the meta-handles, e.g., via a user interface.

Deformation with meta-handles of 3D meshes

Growing community of inventors

La Jolla, CA, United States of America

Minghua Liu