Shapecoder: Discovering abstractions for visual programs from unstructured primitives

RK Jones, P Guerrero, NJ Mitra, D Ritchie - ACM Transactions on …, 2023 - dl.acm.org
We introduce ShapeCoder, the first system capable of taking a dataset of shapes,
represented with unstructured primitives, and jointly discovering (i) useful abstraction …

Neurosymbolic models for computer graphics

D Ritchie, P Guerrero, RK Jones, NJ Mitra… - Computer graphics …, 2023 - Wiley Online Library
Procedural models (ie symbolic programs that output visual data) are a historically‐popular
method for representing graphics content: vegetation, buildings, textures, etc. They offer …

Editing motion graphics video via motion vectorization and transformation

S Zhang, J Ma, J Wu, D Ritchie… - ACM Transactions on …, 2023 - dl.acm.org
Motion graphics videos are widely used in Web design, digital advertising, animated logos
and film title sequences, to capture a viewer's attention. But editing such video is challenging …

Unsupervised deep hashing with fine-grained similarity-preserving contrastive learning for image retrieval

H Cao, L Huang, J Nie, Z Wei - IEEE Transactions on Circuits …, 2023 - ieeexplore.ieee.org
Unsupervised deep hashing has demonstrated significant advancements with the
development of contrastive learning. However, most of previous methods have been …

A Survey of Methods for Converting Unstructured Data to CSG Models

PA Fayolle, M Friedrich - Computer-Aided Design, 2024 - Elsevier
The goal of this document is to survey existing methods for recovering or extracting CSG
(Constructive Solid Geometry) representations from unstructured data such as 3D point …

Re-Thinking Inverse Graphics With Large Language Models

P Kulits, H Feng, W Liu, V Abrevaya… - arXiv preprint arXiv …, 2024 - arxiv.org
Inverse graphics--the task of inverting an image into physical variables that, when rendered,
enable reproduction of the observed scene--is a fundamental challenge in computer vision …

Unsupervised Point Cloud Co-part Segmentation via Co-attended Superpoint Generation and Aggregation

A Umam, CK Yang, JH Chuang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
We propose a co-part segmentation method that takes a set of point clouds of the same
category as input where neither a ground truth label nor a prior network is required. With …

Learning to Edit Visual Programs with Self-Supervision

RK Jones, R Zhang, A Ganeshan, D Ritchie - arXiv preprint arXiv …, 2024 - arxiv.org
We design a system that learns how to edit visual programs. Our edit network consumes a
complete input program and a visual target. From this input, we task our network with …

Physical scene understanding

J Wu - AI Magazine, 2024 - Wiley Online Library
Current AI systems still fail to match the flexibility, robustness, and generalizability of human
intelligence: how even a young child can manipulate objects to achieve goals of their own …

A Unified Differentiable Boolean Operator with Fuzzy Logic

HTD Liu, M Agrawala, C Yuksel, T Omernick… - ACM SIGGRAPH 2024 …, 2024 - dl.acm.org
This paper presents a unified differentiable boolean operator for implicit solid shape
modeling using Constructive Solid Geometry (CSG). Traditional CSG relies on min, max …