CodeNeRF is an implicit 3D neural representation that learns the variation of object shapes
and textures across a category and can be trained, from a set of posed images, to synthesize …

Abstract Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene
representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable …

In this paper we introduce SMPLicit, a novel generative model to jointly represent body
pose, shape and clothing geometry. In contrast to existing learning-based approaches that …

Scene understanding is an active research area. Commercial depth sensors, such as Kinect,
have enabled the release of several RGB-D datasets over the past few years which …

We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object
category models from collections of input images. In contrast to previous work, we are able …

This paper studies the complex task of simultaneous multi-object 3D reconstruction, 6D pose
and size estimation from a single-view RGB-D observation. In contrast to instance-level pose …

We propose DSP-SLAM, an object-oriented SLAM system that builds a rich and accurate
joint map of dense 3D models for foreground objects, and sparse landmark points to …

Reconstructing 3D shape from 2D sketches has long been an open problem because the
sketches only provide very sparse and ambiguous information. In this paper, we use an …

Abstract Human-Object Interactions (HOI) detection, which aims to localize a human and a
relevant object while recognizing their interaction, is crucial for understanding a still image …

We propose a method for in-hand 3D scanning of an unknown object with a monocular
camera. Our method relies on a neural implicit surface representation that captures both the …