Human motion generation aims to generate natural human pose sequences and shows
immense potential for real-world applications. Substantial progress has been made recently …

Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a
model for a single task with supervision. We propose OpenScene, an alternative approach …

We introduce SceneDiffuser, a conditional generative model for 3D scene understanding.
SceneDiffuser provides a unified model for solving scene-conditioned generation …

This paper addresses a novel task of anticipating 3D human-object interactions (HOIs). Most
existing research on HOI synthesis lacks comprehensive whole-body interactions with …

We address the problem of generating realistic 3D motions of humans interacting with
objects in a scene. Our key idea is to create a neural interaction field attached to a specific …

Denoising diffusion models have shown great promise in human motion synthesis
conditioned on natural language descriptions. However, integrating spatial constraints, such …

Learning to generate diverse scene-aware and goal-oriented human motions in 3D scenes
remains challenging due to the mediocre characters of the existing datasets on Human …

Synthesizing semantic-aware, long-horizon, human-object interaction is critical to simulate
realistic human behaviors. In this work, we address the challenging problem of generating …

This paper presents a novel approach to generating the 3D motion of a human interacting
with a target object, with a focus on solving the challenge of synthesizing long-range and …

A long-standing goal in computer vision is to capture, model, and realistically synthesize
human behavior. Specifically, by learning from data, our goal is to enable virtual humans to …