4 citations · 12 across the 8 of their papers we have counts for
8 papers
SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation
Guangda Ji, Qimin Chen, Qinchan Li +3
Single-view 3D generative models have achieved impressive visual quality, yet they are not designed to satisfy structural or functional requirements, and in practice, often fall sh…
Function2Scene: 3D Indoor Scene Layout from Functional Specifications
Ruiqi Wang, Qimin Chen, Daniel Ritchie +4
Most text-driven 3D indoor scene synthesis methods generate rooms from object-centric prompts, asking what furniture should be placed rather than how the space is used. Yet in real…
Functionalization via Structure Completion and Motion Rectification
Mingrui Zhao, Sai Raj Kishore Perla, Kai Wang +8
Acquisition and creation of 3D assets have been largely view- or appearance-driven. As a result, existing digital 3D models often lack the requisite structural components to functi…
Learning to Place Objects with Programs and Iterative Self Training
Adrian Chang, Kai Wang, Yuanbo Li +3
In this work we study indoor scene object placement. Given a 3D indoor scene and an object, the task is to predict placement locations within the scene. Empirical observations of d…
Generating Driving Scenes with Diffusion
Ethan Pronovost, Kai Wang, Nick Roy
In this paper we describe a learned method of traffic scene generation designed to simulate the output of the perception system of a self-driving car. In our "Scene Diffusion" syst…
Roominoes: Generating Novel 3D Floor Plans From Existing 3D Rooms
Kai Wang, Xianghao Xu, Leon Lei +5
Realistic 3D indoor scene datasets have enabled significant recent progress in computer vision, scene understanding, autonomous navigation, and 3D reconstruction. But the scale, di…
The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts
Kai Wang, Paul Guerrero, Vladimir Kim +3
We present the Shape Part Slot Machine, a new method for assembling novel 3D shapes from existing parts by performing contact-based reasoning. Our method represents each shape as a…
Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models
Daniel Ritchie, Kai Wang, Yu-an Lin
We present a new, fast and flexible pipeline for indoor scene synthesis that is based on deep convolutional generative models. Our method operates on a top-down image-based represe…