Convolutional and adversarial networks are found in various fields of knowledge and
activities. One such field is building design, a multi-disciplinary and multi-task process …

We present a simple yet powerful neural network that implicitly represents and renders 3D
objects and scenes only from 2D observations. The network models 3D geometries as a …

View synthesis allows for the generation of new views of a scene given one or more images.
This is challenging; it requires comprehensively understanding the 3D scene from images …

Recognizing scenes and objects in 3D from a single image is a longstanding goal of
computer vision with applications in robotics and AR/VR. For 2D recognition, large datasets …

3D reconstruction is a longstanding ill-posed problem, which has been explored for decades
by the computer vision, computer graphics, and machine learning communities. Since 2015 …

Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not
present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has …

Semantic reconstruction of indoor scenes refers to both scene understanding and object
reconstruction. Existing works either address one part of this problem or focus on …

We present a learning framework that learns to recover the 3D shape, pose and texture from
a single image, trained on an image collection without any ground truth 3D shape, multi …

We study the problem of understanding hand-object interactions from 2D images in the wild.
This requires reconstructing both the hand and the object in 3D, which is challenging …

We present a method that infers spatial arrangements and shapes of humans and objects in
a globally consistent 3D scene, all from a single image in-the-wild captured in an …