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Hooman Shayani

7 accepted papers

2024

Make-A-Shape: a Ten-Million-scale 3D Shape Model

ICML 2024poster

The progression in large-scale 3D generative models has been impeded by significant resource requirements for training and challenges like inefficient representations. This paper introduces Make-A-Shape, a novel 3D generative model trained on a vast scale, using 10 million publicly-available shapes.…

2023

CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes From Natural Language

CVPR 2023poster

Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We introduce CLIP-Sculptor, a method to address these constraints by producing high-fidelity and diverse 3D shapes without the…

Cited by 54SourcePDFScholar
2022

CAPRI-Net: Learning Compact CAD Shapes With Adaptive Primitive Assembly

CVPR 2022poster

We introduce CAPRI-Net, a self-supervised neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies. Given an input 3D shape, our network reconstructs it by an assembly of quadric surface prim…

Cited by 73PDFScholar
2022

UNIST: Unpaired Neural Implicit Shape Translation Network

CVPR 2022poster

We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains. Our model is built on autoencoding implicit fields, rather than point clouds which represents the state of the art. Furthermore, our translation network is tr…

Cited by 9PDFcodeScholar
2021

BRepNet: A Topological Message Passing System for Solid Models

CVPR 2021poster

Boundary representation (B-rep) models are the standard way 3D shapes are described in Computer-Aided Design (CAD) applications. They combine lightweight parametric curves and surfaces with topological information which connects the geometric entities to describe manifolds. In this paper we introduc…

Cited by 116PDFcodeScholar
2021

UV-Net: Learning From Boundary Representations

CVPR 2021poster

We introduce UV-Net, a novel neural network architecture and representation designed to operate directly on Boundary representation (B-rep) data from 3D CAD models. The B-rep format is widely used in the design, simulation and manufacturing industries to enable sophisticated and precise CAD modeling…

Cited by 85PDFcodeScholar
2021

UVStyle-Net: Unsupervised Few-Shot Learning of 3D Style Similarity Measure for B-Reps

ICCV 2021poster

Boundary Representations (B-Reps) are the industry standard in 3D Computer Aided Design/Manufacturing (CAD/CAM) and industrial design due to their fidelity in representing stylistic details. However, they have been ignored in the 3D style research. Existing 3D style metrics typically operate on mesh…

Cited by 7PDFcodeScholar