Adobe, Inc.

Naver Corporation

Thibault Groueix

Radomir Mech

Vladimir Kim

Noam Aigerman

Jun Saito

Philippe Weinzaepfel

Siddhartha Chaudhuri

Matheus Gadelha

Gregory Rogez

Ioannis Kalantidis

Romain Bregier

Kunal Gupta

Marissa Ramirez De Chanlatte

Fabien Baradel

A training system includes: a neural network model configured to determine three-dimensional coordinates of joints, respectively, representing poses of animals in images, where the neural network model is trained using a first training dataset including: images including animals; and coordinates of joints of the animals in the images, respectively; and a training module configured to, after the training of the neural network model using the first training dataset, train the neural network model using a second training dataset including motion capture data, where the motion capture data does not include images of animals and includes measured coordinates at points, respectively, on animals.

Pose estimation systems and methods trained using motion capture data

Methods, systems, and non-transitory computer readable storage media are disclosed for generating digital chain pull paintings in digital images. The disclosed system generate, utilizing a neural network, a plurality of matrices over an ambient space for a plurality of polygons of a three-dimensional mesh based on a plurality of features of the plurality of polygons associated with the three-dimensional mesh. The disclosed system determines a gradient field based on the plurality of matrices of the plurality of polygons. The disclosed system generates a mapping for the three-dimensional mesh based on the gradient field and a differential operator corresponding to the three-dimensional mesh.

Generating mappings of three-dimensional meshes utilizing neural network gradient fields

The present disclosure relates to systems, non-transitory computer-readable media, and methods that tune a 3D-object-reconstruction-machine-learning model to reconstruct 3D models of objects from real images using real images as training data. For instance, the disclosed systems can determine a depth map for a real two-dimensional (2D) image and then reconstruct a 3D model of a digital object in the real 2D image based on the depth map. By using a depth map for a real 2D image, the disclosed systems can generate reconstructed 3D models that better conform to the shape of digital objects in real images than existing systems and use such reconstructed 3D models to generate more realistic looking visual effects (e.g., shadows, relighting).

Reconstructing three-dimensional models of objects from real images based on depth information

Growing community of inventors

Grenoble, France

Thibault Groueix