11 citations · 35 across the 35 of their papers we have counts for
26 papers · 1 filter
Material Magic Wand: Material-Aware Grouping of 3D Parts in Untextured Meshes
Umangi Jain, Vladimir Kim, Matheus Gadelha +2
We introduce the problem of material-aware part grouping in untextured meshes. Many real-world shapes, such as scales of pinecones or windows of buildings, contain repeated structu…
Seeing Through Clutter: Structured 3D Scene Reconstruction via Iterative Object Removal
Rio Aguina-Kang, Kevin James Blackburn-Matzen, Thibault Groueix +2
We present SeeingThroughClutter, a method for reconstructing structured 3D representations from single images by segmenting and modeling objects individually. Prior approaches rely…
Illustrator's Depth: Monocular Layer Index Prediction for Image Decomposition
Nissim Maruani, Peiying Zhang, Siddhartha Chaudhuri +6
We introduce Illustrator's Depth, a novel definition of depth that addresses a key challenge in digital content creation: decomposing flat images into editable, ordered layers. Ins…
Reusing Computation in Text-to-Image Diffusion for Efficient Generation of Image Sets
Dale Decatur, Thibault Groueix, Wang Yifan +3
Text-to-image diffusion models enable high-quality image generation but are computationally expensive. While prior work optimizes per-inference efficiency, we explore an orthogonal…
A Solver-Aided Hierarchical Language for LLM-Driven CAD Design
Benjamin T. Jones, Felix Hähnlein, Zihan Zhang +3
Large language models (LLMs) have been enormously successful in solving a wide variety of structured and unstructured generative tasks, but they struggle to generate procedural geo…
Instant3dit: Multiview Inpainting for Fast Editing of 3D Objects
Amir Barda, Matheus Gadelha, Vladimir G. Kim +3
We propose a generative technique to edit 3D shapes, represented as meshes, NeRFs, or Gaussian Splats, in approximately 3 seconds, without the need for running an SDS type of optim…
SAMa: Material-aware 3D Selection and Segmentation
Michael Fischer, Iliyan Georgiev, Thibault Groueix +3
Decomposing 3D assets into material parts is a common task for artists, yet remains a highly manual process. In this work, we introduce Select Any Material (SAMa), a material selec…
DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement
Qimin Chen, Zhiqin Chen, Vladimir G. Kim +3
We present a 3D modeling method which enables end-users to refine or detailize 3D shapes using machine learning, expanding the capabilities of AI-assisted 3D content creation. Give…
MeshUp: Multi-Target Mesh Deformation via Blended Score Distillation
Hyunwoo Kim, Itai Lang, Noam Aigerman +3
We propose MeshUp, a technique that deforms a 3D mesh towards multiple target concepts, and intuitively controls the region where each concept is expressed. Conveniently, the conce…
Temporal Residual Jacobians For Rig-free Motion Transfer
Sanjeev Muralikrishnan, Niladri Shekhar Dutt, Siddhartha Chaudhuri +4
We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer. Our approach does not assume access to any rigging or intermediate shape k…
Text-guided Controllable Mesh Refinement for Interactive 3D Modeling
Yun-Chun Chen, Selena Ling, Zhiqin Chen +3
We propose a novel technique for adding geometric details to an input coarse 3D mesh guided by a text prompt. Our method is composed of three stages. First, we generate a single-vi…
MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment
Duygu Ceylan, Valentin Deschaintre, Thibault Groueix +5
We present MatAtlas, a method for consistent text-guided 3D model texturing. Following recent progress we leverage a large scale text-to-image generation model (e.g., Stable Diffus…
GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis
Dmitry Petrov, Pradyumn Goyal, Vikas Thamizharasan +5
We introduce GEM3D -- a new deep, topology-aware generative model of 3D shapes. The key ingredient of our method is a neural skeleton-based representation encoding information on b…
As-Plausible-As-Possible: Plausibility-Aware Mesh Deformation Using 2D Diffusion Priors
Seungwoo Yoo, Kunho Kim, Vladimir G. Kim +1
We present As-Plausible-as-Possible (APAP) mesh deformation technique that leverages 2D diffusion priors to preserve the plausibility of a mesh under user-controlled deformation. O…
Neural Semantic Surface Maps
Luca Morreale, Noam Aigerman, Vladimir G. Kim +1
We present an automated technique for computing a map between two genus-zero shapes, which matches semantically corresponding regions to one another. Lack of annotated data prohibi…
Neural Progressive Meshes
Yun-Chun Chen, Vladimir G. Kim, Noam Aigerman +1
The recent proliferation of 3D content that can be consumed on hand-held devices necessitates efficient tools for transmitting large geometric data, e.g., 3D meshes, over the Inter…
TextDeformer: Geometry Manipulation using Text Guidance
William Gao, Noam Aigerman, Thibault Groueix +2
We present a technique for automatically producing a deformation of an input triangle mesh, guided solely by a text prompt. Our framework is capable of deformations that produce bo…
DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization
Richard Liu, Noam Aigerman, Vladimir G. Kim +1
We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization. The motivation for our framework is driven by in…
PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation
Bo Sun, Vladimir G. Kim, Noam Aigerman +2
This paper introduces a data-driven shape completion approach that focuses on completing geometric details of missing regions of 3D shapes. We observe that existing generative meth…
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…
Möbius Convolutions for Spherical CNNs
Thomas W. Mitchel, Noam Aigerman, Vladimir G. Kim +1
Möbius transformations play an important role in both geometry and spherical image processing - they are the group of conformal automorphisms of 2D surfaces and the spherical equiv…
Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases
Jan Bednarik, Noam Aigerman, Vladimir G. Kim +4
We propose a method for unsupervised reconstruction of a temporally-consistent sequence of surfaces from a sequence of time-evolving point clouds. It yields dense and semantically…
GLASS: Geometric Latent Augmentation for Shape Spaces
Sanjeev Muralikrishnan, Siddhartha Chaudhuri, Noam Aigerman +3
We investigate the problem of training generative models on a very sparse collection of 3D models. We use geometrically motivated energies to augment and thus boost a sparse collec…
Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases
Jan Bednarik, Vladimir G. Kim, Siddhartha Chaudhuri +4
We propose a method for the unsupervised reconstruction of a temporally-coherent sequence of surfaces from a sequence of time-evolving point clouds, yielding dense, semantically me…