Search Results for author: Daniel Ritchie

Found 24 papers, 13 papers with code

Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections

no code implementations17 Jun 2022 Xianghao Xu, Yifan Ruan, Srinath Sridhar, Daniel Ritchie

We operationalize this concept with an algorithm that optimizes a shape's part motion parameters such that it can transform into other shapes in the collection.

Motion Detection motion prediction +1

StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement

1 code implementation13 Feb 2022 Zheng Zhang, Ying Xu, Yanhao Wang, Bingsheng Yao, Daniel Ritchie, Tongshuang Wu, Mo Yu, Dakuo Wang, Toby Jia-Jun Li

Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions.


Learning Body-Aware 3D Shape Generative Models

no code implementations13 Dec 2021 Bryce Blinn, Alexander Ding, R. Kenny Jones, Manolis Savva, Srinath Sridhar, Daniel Ritchie

The body-shape-conditioned models produce chairs which will be comfortable for a person with the given body shape; the pose-conditioned models produce chairs which accommodate the given sitting pose.

Roominoes: Generating Novel 3D Floor Plans From Existing 3D Rooms

no code implementations10 Dec 2021 Kai Wang, Xianghao Xu, Leon Lei, Selena Ling, Natalie Lindsay, Angel X. Chang, Manolis Savva, Daniel Ritchie

We then discuss different strategies for solving the problem, and design two representative pipelines: one uses available 2D floor plans to guide selection and deformation of 3D rooms; the other learns to retrieve a set of compatible 3D rooms and combine them into novel layouts.

3D Reconstruction Autonomous Navigation +1

The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts

no code implementations1 Dec 2021 Kai Wang, Paul Guerrero, Vladimir Kim, Siddhartha Chaudhuri, Minhyuk Sung, Daniel Ritchie

We present the Shape Part Slot Machine, a new method for assembling novel 3D shapes from existing parts by performing contact-based reasoning.

Learning to Infer Kinematic Hierarchies for Novel Object Instances

no code implementations15 Oct 2021 Hameed Abdul-Rashid, Miles Freeman, Ben Abbatematteo, George Konidaris, Daniel Ritchie

Manipulating an articulated object requires perceiving itskinematic hierarchy: its parts, how each can move, and howthose motions are coupled.

Instance Segmentation Semantic Segmentation

The Neurally-Guided Shape Parser: Grammar-based Labeling of 3D Shape Regions with Approximate Inference

1 code implementation CVPR 2022 R. Kenny Jones, Aalia Habib, Rana Hanocka, Daniel Ritchie

We propose the Neurally-Guided Shape Parser (NGSP), a method that learns how to assign fine-grained semantic labels to regions of a 3D shape.

Semantic Segmentation

ShapeMOD: Macro Operation Discovery for 3D Shape Programs

1 code implementation13 Apr 2021 R. Kenny Jones, David Charatan, Paul Guerrero, Niloy J. Mitra, Daniel Ritchie

In this paper, we present ShapeMOD, an algorithm for automatically discovering macros that are useful across large datasets of 3D shape programs.

Shape From Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracing

1 code implementation6 Dec 2020 Purvi Goel, Loudon Cohen, James Guesman, Vikas Thamizharasan, James Tompkin, Daniel Ritchie

In this paper, we explore how to use differentiable ray tracing to refine an initial coarse mesh and per-mesh-facet material representation.

PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate Distributions

1 code implementation CVPR 2022 R. Kenny Jones, Homer Walke, Daniel Ritchie

Related to these approaches, we introduce a novel self-training variant unique to program inference, where program pseudo-labels are paired with their executed output shapes, avoiding label mismatch at the cost of an approximate shape distribution.

Self-Supervised Learning

GANHopper: Multi-Hop GAN for Unsupervised Image-to-Image Translation

1 code implementation ECCV 2020 Wallace Lira, Johannes Merz, Daniel Ritchie, Daniel Cohen-Or, Hao Zhang

Instead of executing translation directly, we steer the translation by requiring the network to produce in-between images that resemble weighted hybrids between images from the input domains.

Translation Unsupervised Image-To-Image Translation

Learning to Describe Scenes with Programs

no code implementations ICLR 2019 Yunchao Liu, Zheng Wu, Daniel Ritchie, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu

We are able to understand the higher-level, abstract regularities within the scene such as symmetry and repetition.

Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models

1 code implementation CVPR 2019 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.

Indoor Scene Synthesis

Improving Shape Deformation in Unsupervised Image-to-Image Translation

4 code implementations ECCV 2018 Aaron Gokaslan, Vivek Ramanujan, Daniel Ritchie, Kwang In Kim, James Tompkin

Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically unsuccessful when the domains require larger shape change.

Semantic Segmentation Translation +1

ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans

no code implementations CVPR 2018 Angela Dai, Daniel Ritchie, Martin Bokeloh, Scott Reed, Jürgen Sturm, Matthias Nießner

We introduce ScanComplete, a novel data-driven approach for taking an incomplete 3D scan of a scene as input and predicting a complete 3D model along with per-voxel semantic labels.

Semantic Segmentation

An Improved Training Procedure for Neural Autoregressive Data Completion

no code implementations23 Nov 2017 Maxime Voisin, Daniel Ritchie

In this paper, we provide evidence that the order-agnostic (OA) training procedure is suboptimal for data completion.

Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks

1 code implementation NeurIPS 2016 Daniel Ritchie, Anna Thomas, Pat Hanrahan, Noah D. Goodman

Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) provide powerful tools for constraining procedural models in computer graphics, but they require many samples to produce desirable results.

C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching

no code implementations7 Sep 2015 Daniel Ritchie, Andreas Stuhlmüller, Noah D. Goodman

Lightweight, source-to-source transformation approaches to implementing MCMC for probabilistic programming languages are popular for their simplicity, support of existing deterministic code, and ability to execute on existing fast runtimes.

Probabilistic Programming

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