372 citations · 1k across the 31 of their papers we have counts for
35 papers
GSComplete: Gaussian Splat Completion with 2D Diffusion Priors
Elias Brugger, Philipp Erler, Stefan Ohrhallinger +1
Gaussian splats provide a fast, high-fidelity representation for 3D objects but are often constructed from incomplete input data in practice, leaving missing regions. Existing comp…
PhotoMat: A Material Generator Learned from Single Flash Photos
Xilong Zhou, Miloš Hašan, Valentin Deschaintre +4
Authoring high-quality digital materials is key to realism in 3D rendering. Previous generative models for materials have been trained exclusively on synthetic data; such data is l…
ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives
R. Kenny Jones, Paul Guerrero, Niloy J. Mitra +1
Programs are an increasingly popular representation for visual data, exposing compact, interpretable structure that supports manipulation. Visual programs are usually written in do…
Generating Procedural Materials from Text or Image Prompts
Yiwei Hu, Paul Guerrero, Miloš Hašan +2
Node graph systems are used ubiquitously for material design in computer graphics. They allow the use of visual programming to achieve desired effects without writing code. As high…
Neurosymbolic Models for Computer Graphics
Daniel Ritchie, Paul Guerrero, R. Kenny Jones +4
Procedural models (i.e. symbolic programs that output visual data) are a historically-popular method for representing graphics content: vegetation, buildings, textures, etc. They o…
Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly
Xianghao Xu, Paul Guerrero, Matthew Fisher +2
Representing a 3D shape with a set of primitives can aid perception of structure, improve robotic object manipulation, and enable editing, stylization, and compression of 3D shapes…
3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models
Gimin Nam, Mariem Khlifi, Andrew Rodriguez +3
Diffusion models have shown great promise for image generation, beating GANs in terms of generation diversity, with comparable image quality. However, their application to 3D shape…
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
Titas Anciukevičius, Zexiang Xu, Matthew Fisher +4
Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support task…
Search for Concepts: Discovering Visual Concepts Using Direct Optimization
Pradyumna Reddy, Paul Guerrero, Niloy J. Mitra
Finding an unsupervised decomposition of an image into individual objects is a key step to leverage compositionality and to perform symbolic reasoning. Traditionally, this problem…
MatFormer: A Generative Model for Procedural Materials
Paul Guerrero, Miloš Hašan, Kalyan Sunkavalli +3
Procedural material graphs are a compact, parameteric, and resolution-independent representation that are a popular choice for material authoring. However, designing procedural mat…
NeuForm: Adaptive Overfitting for Neural Shape Editing
Connor Z. Lin, Niloy J. Mitra, Gordon Wetzstein +2
Neural representations are popular for representing shapes, as they can be learned form sensor data and used for data cleanup, model completion, shape editing, and shape synthesis.…
Node Graph Optimization Using Differentiable Proxies
Yiwei Hu, Paul Guerrero, Miloš Hašan +2
Graph-based procedural materials are ubiquitous in content production industries. Procedural models allow the creation of photorealistic materials with parametric control for flexi…
Controlling Material Appearance by Examples
Yiwei Hu, Miloš Hašan, Paul Guerrero +2
Despite the ubiquitousness of materials maps in modern rendering pipelines, their editing and control remains a challenge. In this paper, we present an example-based material contr…
TileGen: Tileable, Controllable Material Generation and Capture
Xilong Zhou, Miloš Hašan, Valentin Deschaintre +3
Recent methods (e.g. MaterialGAN) have used unconditional GANs to generate per-pixel material maps, or as a prior to reconstruct materials from input photographs. These models can…
COFS: Controllable Furniture layout Synthesis
Wamiq Reyaz Para, Paul Guerrero, Niloy Mitra +1
Scalable generation of furniture layouts is essential for many applications in virtual reality, augmented reality, game development and synthetic data generation. Many existing met…
Neural Convolutional Surfaces
Luca Morreale, Noam Aigerman, Paul Guerrero +2
This work is concerned with a representation of shapes that disentangles fine, local and possibly repeating geometry, from global, coarse structures. Achieving such disentanglement…
LayoutEnhancer: Generating Good Indoor Layouts from Imperfect Data
Kurt Leimer, Paul Guerrero, Tomer Weiss +1
We address the problem of indoor layout synthesis, which is a topic of continuing research interest in computer graphics. The newest works made significant progress using data-driv…
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…
Differentiable Surface Triangulation
Marie-Julie Rakotosaona, Noam Aigerman, Niloy Mitra +2
Triangle meshes remain the most popular data representation for surface geometry. This ubiquitous representation is essentially a hybrid one that decouples continuous vertex locati…
SketchGen: Generating Constrained CAD Sketches
Wamiq Reyaz Para, Shariq Farooq Bhat, Paul Guerrero +4
Computer-aided design (CAD) is the most widely used modeling approach for technical design. The typical starting point in these designs is 2D sketches which can later be extruded a…
ShapeMOD: Macro Operation Discovery for 3D Shape Programs
R. Kenny Jones, David Charatan, Paul Guerrero +2
A popular way to create detailed yet easily controllable 3D shapes is via procedural modeling, i.e. generating geometry using programs. Such programs consist of a series of instruc…
Learning Delaunay Surface Elements for Mesh Reconstruction
Marie-Julie Rakotosaona, Paul Guerrero, Noam Aigerman +2
We present a method for reconstructing triangle meshes from point clouds. Existing learning-based methods for mesh reconstruction mostly generate triangles individually, making it…
Generative Layout Modeling using Constraint Graphs
Wamiq Para, Paul Guerrero, Tom Kelly +2
We propose a new generative model for layout generation. We generate layouts in three steps. First, we generate the layout elements as nodes in a layout graph. Second, we compute c…
Discovering Pattern Structure Using Differentiable Compositing
Pradyumna Reddy, Paul Guerrero, Matt Fisher +2
Patterns, which are collections of elements arranged in regular or near-regular arrangements, are an important graphic art form and widely used due to their elegant simplicity and…