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Affiliations
Brown University
Computer Science Department
Brown Visual Computing
Contact Information
CIT, Room 445
115 Waterman Street
Providence, RI 02912
Office hours: Book a slot
Info for prospective students
       
Teaching
Fall 2026
CSCI 1230: Introduction to Computer Graphics
Spring 2026
CSCI 2240: Advanced Computer Graphics
Fall 2025
CSCI 1230: Introduction to Computer Graphics
Spring 2025
CSCI 2240: Advanced Computer Graphics
Talks & Videos
Synthesizing Abstractions for 3D Modeling

Talk given at the BeyondCAD Workshop at The University of Edinburgh, July 2026.

How Video Games Saved Math (for me)

Invited talk at Brown's Symposium for Undergraduates in the Mathematical Sciences (SUMS) 2026.

Deep Learning for 3D Geometry

SGP 2024 Summer School (with Zoë Marschner)

Learning to Represent Shapes with Programs

SGP 2022 Summer School

Neurosymbolic Generative Models for Structured 3D Content

3D Geometry and Vision (3DGV) Seminar

We Need a Deeper Network

Brown CSCI 1470/2470 Fall 2020 end-of-semester song

Probabilistic Programming for Computer Graphics

PROBPROG 2018

Resources
Deep Learning for 3D Geometry

My slides for the course that Zoë Marschner and I taught at SGP '24

The SIGGRAPH Logo Gallery

An interactive gallery of paper figures featuring the SIGGRAPH name and/or logo

Optimeet

A tool for coordinated scheduling of multiple meetings

REU Site: AI for Computational Creativity

Website for our NSF REU Site

exploreCSR: Socially-Reponsible AI for Computational Creativity

Website for our undergrad research experience program (in partnership with Google Research)

Learning to Generate 3D Shapes and Scenes

Website for our CVPR 2021 Workshop

Learning 3D Generative Models

Website for our CVPR 2020 Workshop

Learning Generative Models of 3D Structures

Slides from our Eurographics 2019 Tutorial

Three-phase Paper Writing

An algorithm for writing technical papers

About

I am an Associate Professor of Computer Science at Brown University, where I co-lead the Brown Visual Computing group. My research sits at the intersection of computer graphics, artificial intelligence, and machine learning—especially how AI and ML tools can make the practice of visual creation easier, more accessible, and more fulfilling. I'm particularly interested in technologies for creating, manipulating, and analyzing 3D shapes and scenes, and these days I'm excited about neurosymbolic methods for doing so (combining the best aspects of procedural models and deep neural networks). My research has been funded by gifts from Adobe, Autodesk, Roblox, Pixar, and NVIDIA, as well as grants from DARPA and the National Science Foundation, including an NSF CAREER Award.

I received my PhD from Stanford University, where I worked with Pat Hanrahan in the Graphics Lab and with Noah Goodman in the Computation and Cognition Lab. I received my undergraduate degree from the University of California, Berkeley.

My Erdős number is 4 (Daniel Ritchie → Niloy Mitra → Leonidas Guibas → Boris Aronov → Paul Erdős). My Bacon number is 3, though only if you count all film credits, not just cast appearances (Daniel Ritchie → John Lasseter → Tom Hanks → Kevin Bacon). This (debatably) makes my Erdős-Bacon number 7.

News
November 2026
Applications are open for our NSF summer REU site, Artificial Intelligence for Computational Creativity! The application deadline is February 3.
September 2026
Code for ShapeLib is now available on Github.
September 2026
Check out Maxim's new paper (to appear at SIGGRAPH Asia) to learn how a good DSL design + classic program synthesis techniques can beat frontier LLMs at generating game character behaviors.
August 2026
The ShapeLib paper is finally out in ACM ToG!
July 2026
A recording of my talk at the BeyondCAD Workshop is now available.
Archive
Publications
Synthesizing Reactive Character Behaviors for Continuous Games via Programmatic Policy Search
Maxim Gumin, Hsueh-Ti Derek Liu, Victor Zordan, and Daniel Ritchie
SIGGRAPH Asia 2026
arxiv
ImplicitDrag: Drag-Based Editing of Differentiable Implicits
Henro Kriel, Marzia Riso, Aamen Muharram, Daniel Ritchie, and Adrien Bousseau
Pacific Graphics 2026
paper
ShapeLib: Designing a library of programmatic 3D shape abstractions with Large Language Models
R. Kenny Jones, Paul Guerrero, Niloy Mitra, and Daniel Ritchie
ACM Transactions on Graphics 2026
paper | website | code
Matérn Noise for Triangulation-Agnostic Flow Matching on Meshes
Tianshu Kuai, Arman Maesumi, Daniel Ritchie, and Noam Aigerman
SIGGRAPH 2026
website | paper | arxiv | code
Gradient-based Approximation of Nonuniform Low-Disrepancy Samples
Xiangyu Li, Ege Ciklabakkal, Daniel Ritchie, and Toshiya Hachisuka
SIGGRAPH 2026
paper
Residual Primitive Fitting of 3D Shapes with SuperFrusta
Aditya Ganeshan, Mateus Gadelha, Thibault Groueix, Zhiqin Chen, Siddhartha Chaudhuri, Vladimir Kim, Yifan Wang, and Daniel Ritchie
CVPR 2026
website | paper | arxiv | code
Oral Presentation
Learning to Place Objects with Programs and Iterative Self Training
Adrian Chang, Kai Wang, Yuanbo Li, Manolis Savva, Angel X. Chang, and Daniel Ritchie
CVPR 2026 Workshop on AI for Content Creation
arxiv
PLLM: Pseudo-Labeling Large Language Models for CAD Program Synthesis
Yuanbo Li, Dule Shu, Yan-Ying Chen, Matt Klenk, and Daniel Ritchie
CVPR 2026 Workshop on Synthetic Data for Computer Vision
paper
ProcTex: Consistent and Interactive Text-to-texture Synthesis for Part-based Procedural Models
Ruiqi Xu, Zihan Zhu, Benjamin Ahlbrand, Srinath Sridhar, and Daniel Ritchie
Eurographics 2026
paper
CADrawer: Autoregressive CAD Generation from 3D Sketches
Yuanbo Li, Gilda Manfredi, Henro Kriel, Chengye Hao, Xianghao Xu, Adrien Bousseau, and Daniel Ritchie
Eurographics 2026
paper
GenHSI: Controllable Generation of Human-Scene Interaction Videos
Zekun Li, Rui Zhou, Rahul Sajnani, Xiaoyan Cong, Daniel Ritchie, and Srinath Sridhar
WACV 2026
project page | arxiv | code
Procedural Scene Programs for Open-Universe Scene Generation: LLM-Free Error Correction via Program Search
Maxim Gumin, Do Heon Han, Seung Jean Yoo, Aditya Ganeshan, R. Kenny Jones, Kailiang Fu, Rio Aguina-Kang, Stewart Morris, and Daniel Ritchie
SIGGRAPH Asia 2025
arxiv
PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples
Junyu Liu, R. Kenny Jones, and Daniel Ritchie
SIGGRAPH Asia 2025
project page | arxiv | code
MiGumi: Making Tightly Coupled Integral Joints Millable
Aditya Ganeshan, Kurt Fleischer, Wenzel Jakob, Ariel Shamir, Daniel Ritchie, Takeo Igarashi, and Maria Larsson
SIGGRAPH Asia 2025
project page | paper | code | dataset
PoissonNet: A Local-Global Approach for Learning on Surfaces
Arman Maesumi, Tanish Makadia, Thibault Groueix, Vladimir Kim, Daniel Ritchie, and Noam Aigerman
SIGGRAPH Asia 2025
project page | paper | code
Diorama: Unleashing Zero-shot Single-view 3D Scene Modeling
Qirui Wu, Denys Iliash, Daniel Ritchie, Manolis Savva, and Angel X. Chang
ICCV 2025
project page | arXiv
Highlight
GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities
Rao Fu, Dingxi Zhang, Alex Jiang, Wanjia Fu, Austin Funk, Daniel Ritchie, and Srinath Sridhar
CVPR 2025
project page | arXiv
Highlight
Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy
Aditya Ganeshan, Thibault Groueix, Paul Guerrero, Radomír Měch, Matthew Fisher, and Daniel Ritchie
CVPR 2025
project page | arXiv
Learning to Edit Visual Programs with Self-Supervision
R. Kenny Jones, Renhao Zhang, Aditya Ganeshan, and Daniel Ritchie
NeurIPS 2024
project page | arXiv | code
ParSEL: Parameterized Shape Editing with Language
Aditya Ganeshan, Ryan Y. Huang, Xianghao Xu, R. Kenny Jones, and Daniel Ritchie
SIGGRAPH Asia 2024
project page | arXiv
CLIPtortionist: Zero-shot Text-driven Deformation for Manufactured 3D Shapes
Xianghao Xu, Srinath Sridhar, and Daniel Ritchie
arXiv preprint
arXiv
R3DS: Reality-linked 3D Scenes for Panoramic Scene Understanding
Qirui Wu, Sonia Raychaudhuri, Daniel Ritchie, Manolis Savva, and Angel X. Chang
EECV 2024
project page | arXiv
One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns
Arman Maesumi, Dylan Hu, Krishi Saripalli, Vladimir Kim, Matthew Fisher, Sören Pirk, and Daniel Ritchie
SIGGRAPH 2024
project page | arXiv | paper
Learning to Infer Generative Template Programs for Visual Concepts
R. Kenny Jones, Siddhartha Chaudhuri, and Daniel Ritchie
ICML 2024
project page | arXiv
PossibleImpossibles: Exploratory Procedural Design of Impossible Structures
Yuanbo Li, Tianyi Ma, Zaineb Aljumayaat, and Daniel Ritchie
Eurographics 2024
paper | code
CharacterMixer: Rig-Aware Interpolation of 3D Characters
Xiao Zhan, Rao Fu, and Daniel Ritchie
Eurographics 2024
project page | arXiv | paper
Generalizing Single-View 3D Shape Retrieval to Occlusions and Unseen Objects
Qirui Wu, Daniel Ritchie, Manolis Savva, and Angel X. Chang
3DV 2024
project page | arXiv
Open-Universe Indoor Scene Generation using LLM Program Synthesis and Uncurated Object Databases
Rio Aguina-Kang, Maxim Gumin, Do Heon Han, Stewart Morris, Seung Jean Yoo, Aditya Ganeshan, R. Kenny Jones, Qiuhong Anna Wei, Kailiang Fu, and Daniel Ritchie
arXiv preprint
arXiv
Editing Motion Graphics Videos via Motion Vectorization & Transformation
Sharon Zhang, Jiaju Ma, Daniel Ritchie, Jiajun Wu, and Maneesh Agrawala
SIGGRAPH Asia 2023
project page | arXiv
Explorable Mesh Deformation Subspaces from Unstructured 3D Generative Models
Arman Maesumi, Paul Guerrero, Vladimir Kim, Matthew Fisher, Siddhartha Chaudhuri, Noam Aigerman, and Daniel Ritchie
SIGGRAPH Asia 2023
project page | paper | arXiv
Improving Unsupervised Visual Program Inference with Code Rewriting Families
Aditya Ganeshan, R. Kenny Jones, and Daniel Ritchie
ICCV 2023
project page | paper | arXiv
Oral Presentation
ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives
R. Kenny Jones, Paul Guerrero, Niloy Mitra, and Daniel Ritchie
SIGGRAPH 2023
project page | arXiv
Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly
Xianghao Xu, Paul Guerrero, Matthew Fisher, Siddhartha Chaudhuri, and Daniel Ritchie
CVPR 2023
arXiv
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language
Aditya Sanghi, Rao Fu, Vivian Liu, Karl D. D. Willis, Hooman Shayani, Amir Hosein Khasahmadi, Srinath Sridhar, and Daniel Ritchie
CVPR 2023
project page | arXiv
Neurosymbolic Models for Computer Graphics
Daniel Ritchie, Paul Guerrero, R. Kenny Jones, Niloy Mitra, Adriana Schulz, Karl D. D. Willis, and Jiajun Wu
Eurographics 2023 STAR
arXiv | paper
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model
Rao Fu, Xiao Zhan, Yiwen Chen, Daniel Ritchie, and Srinath Sridhar
NeurIPS 2022
arXiv | project page
SHRED: 3D Shape Region Decomposition with Learned Local Operations
R. Kenny Jones, Aalia Habib, and Daniel Ritchie
SIGGRAPH Asia 2022
arXiv | project page
The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts
Kai Wang, Paul Guerrero, Vladimir Kim, Siddhartha Chaudhuri, Minhyuk Sung, and Daniel Ritchie
ECCV 2022
arXiv
Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections
Xianghao Xu, Yifan Ruan, Srinath Sridhar, and Daniel Ritchie
SIGGRAPH 2022
paper | arXiv | code
PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate Distributions
R. Kenny Jones, Homer Walke, and Daniel Ritchie
CVPR 2022
project page | arXiv
The Neurally-Guided Shape Parser: Grammar-based Labeling of 3D Shape Regions with Approximate Inference
R. Kenny Jones, Aalia Habib, Rana Hanocka, and Daniel Ritchie
CVPR 2022
project page | arXiv
Learning to Infer Kinematic Hierarchies for Novel Object Instances
Hameed Abdul-Rashid, Miles Freeman, Ben Abbatematteo, George Konidaris, and Daniel Ritchie
ICRA 2022
arXiv
Learning Body-Aware 3D Shape Generative Models
Bryce Blinn, Alexander Ding, R. Kenny Jones, Manolis Savva, Srinath Sridhar, and Daniel Ritchie
arXiv preprint
arXiv
ShapeMOD: Macro Operation Discovery for 3D Shape Programs
R. Kenny Jones, David Charatan, Paul Guerrero, Niloy Mitra, and Daniel Ritchie
SIGGRAPH 2021
project page | arXiv
Roominoes: Generating Novel 3D Floor Plans From Existing 3D Rooms
Kai Wang, Xianghao Xu, Leon Lei, Natalie Lindsay, Selena Ling, Angel X. Chang, Manolis Savva, and Daniel Ritchie
SGP 2021
paper | talk
Inferring CAD Modeling Sequences using Zone Graphs
Xianghao Xu, Wenzhe Peng, Chin-Yi Cheng, Karl D. D. Willis, and Daniel Ritchie
CVPR 2021
project page | arXiv
Shape from Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracing
Purvi Goel, Loudon Cohen, James Guesman, Vikas Thamizharasan, James Tompkin, and Daniel Ritchie
3DV 2020
paper | project page
Motion Annotation Programs: A Scalable Approach to Annotating Kinematic Articulations in Large 3D Shape Collections
Xianghao Xu, David Charatan, Sonia Raychaudhuri, Hanxiao Jiang, Mae Heitmann, Vladimir Kim, Siddhartha Chaudhuri, Manolis Savva, Angel X. Chang, and Daniel Ritchie
3DV 2020
paper | project page
ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis
R. Kenny Jones, Theresa Barton, Xianghao Xu, Kai Wang, Ellen Jiang, Paul Guerrero, Niloy Mitra, and Daniel Ritchie
SIGGRAPH Asia 2020
paper | project page
GANHopper: Multi-Hop GAN for Unsupervised Image-to-Image Translation
Wallace Lira, Johannes Merz, Daniel Ritchie, Daniel Cohen-Or, and Hao Zhang
ECCV 2020
arXiv
Learning Generative Models of 3D Structures
Siddhartha Chaudhuri, Daniel Ritchie, Jiajun Wu, Kai Xu, and Hao Zhang
Eurographics 2020 STAR
paper | presentation video
Learning Style Compatibility Between Objects in a Real-World 3D Asset Database
Yifan Liu, Ruolan Tang, and Daniel Ritchie
Pacific Graphics 2019
paper | supplemental
PlanIT: Planning and Instantiating Indoor Scenes with Relation Graph and Spatial Prior Networks
Kai Wang, Yu-an Lin, Ben Weissmann, Manolis Savva, Angel X. Chang, and Daniel Ritchie
SIGGRAPH 2019
paper | fast forward video
Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models
Daniel Ritchie, Kai Wang, and Yu-an Lin
CVPR 2019
arXiv
Learning to Describe Scenes with Programs
Yunchao Liu, Zheng Wu, Daniel Ritchie, William T. Freeman, Joshua B. Tenenbaum, and Jiajun Wu
ICLR 2019
OpenReview
Learning to Infer Graphics Programs from Hand-Drawn Images
Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, and Joshua B. Tenenbaum
NeurIPS 2018
arXiv
Spotlight Presentation
Improving Shape Deformation in Unsupervised Image-to-Image Translation
Aaron Gokaslan, Vivek Ramanujan, Daniel Ritchie, Kwang In Kim, and James Tompkin
ECCV 2018
arXiv | code
Deep Convolutional Priors for Indoor Scene Synthesis
Kai Wang, Manolis Savva, Angel X. Chang, and Daniel Ritchie
SIGGRAPH 2018
pdf | code
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
Angela Dai, Daniel Ritchie, Martin Bokeloh, Scott Reed, Jürgen Sturm, and Matthias Nießner
CVPR 2018
arXiv | video
Example-based Authoring of Procedural Modeling Programs with Structural and Continuous Variability
Daniel Ritchie, Sarah Jobalia, and Anna Thomas
Eurographics 2018
pdf | supplemental
An Improved Training Procedure for Neural Autoregressive Data Completion
Maxime Voisin and Daniel Ritchie
NIPS 2017 Bayesian Deep Learning Workshop
arXiv | code
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks
Daniel Ritchie, Anna Thomas, Pat Hanrahan, and Noah D. Goodman
NIPS 2016
pdf | supplemental | video | demo | arXiv
Deep Amortized Inference for Probabilistic Programs
Daniel Ritchie, Paul Horsfall, and Noah D. Goodman
arXiv preprint
pdf | arXiv
Probabilistic Programming for Procedural Modeling and Design
Daniel Ritchie
Stanford University Doctoral Dissertation 2016
pdf | stanford library
C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching
Daniel Ritchie, Andreas Stuhlmüller, and Noah D. Goodman
AISTATS 2016
pdf | slides | code
Controlling Procedural Modeling Programs with Stochastically-Ordered Sequential Monte Carlo
Daniel Ritchie, Ben Mildenhall, Noah D. Goodman, and Pat Hanrahan
SIGGRAPH 2015
pdf | slides | code | demo
Generating Design Suggestions under Tight Constraints with Gradient-based Probabilistic Programming
Daniel Ritchie, Sharon Lin, Noah D. Goodman, and Pat Hanrahan
Eurographics 2015
pdf | slides | video | code
Best Paper Honorable Mention
Quicksand: A Lightweight Embedding of Probabilistic Programming for Procedural Modeling and Design
Daniel Ritchie
3rd NIPS Workshop on Probabilistic Programming
pdf | slides | project page | talk
First-class Runtime Generation of High-performance Types using Exotypes
Zachary Devito, Daniel Ritchie, Matthew Fisher, and Pat Hanrahan
PLDI 2014
pdf | project page
Probabilistic Color-by-Numbers: Suggesting Pattern Colorizations Using Factor Graphs
Sharon Lin, Daniel Ritchie, Matthew Fisher, and Pat Hanrahan
SIGGRAPH 2013
pdf | project page
Example-based Synthesis of 3D Object Arrangements
Matthew Fisher, Daniel Ritchie, Manolis Savva, Thomas Funkhouser, and Pat Hanrahan
SIGGRAPH Asia 2012
pdf | project page
d.tour: Style-based Exploration of Design Example Galleries
Daniel Ritchie, Ankita Kejriwal, and Scott R. Klemmer
UIST 2011
pdf | video
Dynamic Local Remeshing for Elastoplastic Simulation
Martin Wicke, Daniel Ritchie, Bryan M. Klingner, Sebastian Burke, Jonathan R. Shewchuk, and James F. O'Brien
SIGGRAPH 2010
pdf | video 1 | video 2 | project page
Interactive Simulation of Surgical Needle Insertion and Steering
Nuttapong Chentanez, Ron Alterovitz, Daniel Ritchie, Lita Cho, Kris Hauser, Ken Goldberg, Jonathan R. Shewchuk, and James F. O'Brien
SIGGRAPH 2009
pdf | video 1 | project page
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