87 citations · 213 across the 18 of their papers we have counts for
24 papers
DynaMesh: Dynamic 3D Texture Generation
Raj Hansini, Guan Chen, Rana Hanocka +1
We present DynaMesh, a dynamic texture generation method for 3D meshes. Given a textureless shape and a text prompt describing an effect, our method produces an appearance that evo…
RADmesh: Remesh-Aware Mesh Deformation
Nam Anh Dinh, Itai Lang, Oded Stein +1
We propose a remeshing-enhanced method for generatively deforming shapes with visual losses. It is intuitive that sufficiently drastic deformations of a mesh without changing its t…
MeshFM: 2D Features Are All You Need for 3D Shape Understanding
Jinfan Zhou, Richard Liu, Itai Lang +1
We present MeshFM, an efficient feedforward framework for extracting rich features from 3D inputs. Our method distills 2D features from visual foundation models into 3D. We train a…
HandMade: Spatial Prompting for Generative 3D Creation with Part-Labeled VR Sketches
Jialin Huang, Rana Hanocka, Ariel Shamir +1
Text-to-3D generation lowers the barrier to 3D content creation, but text alone is a weak interface for specifying spatial intent: where parts should be placed, how they relate, an…
Look Both Ways Before You Cross: Lifting Cross Fields From 2D Visual Priors
Dale Decatur, Jacob Serfaty, Oded Stein +2
We present CrossLift, a technique for computing cross fields on meshes guided by visual features in images. We leverage powerful text-to-image priors that are capable of synthesizi…
MeshOn: Intersection-Free Mesh-to-Mesh Composition
Hyunwoo Kim, Itai Lang, Hadar Averbuch-Elor +2
We propose MeshOn, a method that finds physically and semantically realistic compositions of two input meshes. Given an accessory, a base mesh with a user-defined target region, an…
Deep Feature Deformation Weights
Richard Liu, Itai Lang, Rana Hanocka
Handle-based mesh deformation is a classic paradigm in computer graphics which enables intuitive edits from sparse controls. Classical techniques are fast and precise, but require…
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…
LL3M: Large Language 3D Modelers
Sining Lu, Guan Chen, Nam Anh Dinh +3
We present LL3M, a multi-agent system that leverages pretrained large language models (LLMs) to generate 3D assets by writing interpretable Python code in Blender. We break away fr…
3D PixBrush: Image-Guided Local Texture Synthesis
Dale Decatur, Itai Lang, Kfir Aberman +1
We present 3D PixBrush, a method for performing image-driven edits of local regions on 3D meshes. 3D PixBrush predicts a localization mask and a synthesized texture that faithfully…
WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstraction
Richard Liu, Daniel Fu, Noah Tan +2
In this work we present WIR3D, a technique for abstracting 3D shapes through a sparse set of visually meaningful curves in 3D. We optimize the parameters of Bezier curves such that…
Geometry in Style: 3D Stylization via Surface Normal Deformation
Nam Anh Dinh, Itai Lang, Hyunwoo Kim +2
We present Geometry in Style, a new method for identity-preserving mesh stylization. Existing techniques either adhere to the original shape through overly restrictive deformations…
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…
HaLo-NeRF: Learning Geometry-Guided Semantics for Exploring Unconstrained Photo Collections
Chen Dudai, Morris Alper, Hana Bezalel +3
Internet image collections containing photos captured by crowds of photographers show promise for enabling digital exploration of large-scale tourist landmarks. However, prior work…
iSeg: Interactive 3D Segmentation via Interactive Attention
Itai Lang, Fei Xu, Dale Decatur +2
We present iSeg, a new interactive technique for segmenting 3D shapes. Previous works have focused mainly on leveraging pre-trained 2D foundation models for 3D segmentation based o…
3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation
Dale Decatur, Itai Lang, Kfir Aberman +1
In this work we develop 3D Paintbrush, a technique for automatically texturing local semantic regions on meshes via text descriptions. Our method is designed to operate directly on…
TEDi: Temporally-Entangled Diffusion for Long-Term Motion Synthesis
Zihan Zhang, Richard Liu, Kfir Aberman +1
The gradual nature of a diffusion process that synthesizes samples in small increments constitutes a key ingredient of Denoising Diffusion Probabilistic Models (DDPM), which have p…
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…
Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models
Shawn Shan, Jenna Cryan, Emily Wenger +3
Recent text-to-image diffusion models such as MidJourney and Stable Diffusion threaten to displace many in the professional artist community. In particular, models can learn to mim…
3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions
Dale Decatur, Itai Lang, Rana Hanocka
We present 3D Highlighter, a technique for localizing semantic regions on a mesh using text as input. A key feature of our system is the ability to interpret "out-of-domain" locali…
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…
GANimator: Neural Motion Synthesis from a Single Sequence
Peizhuo Li, Kfir Aberman, Zihan Zhang +2
We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements o…
Text2Mesh: Text-Driven Neural Stylization for Meshes
Oscar Michel, Roi Bar-On, Richard Liu +2
In this work, we develop intuitive controls for editing the style of 3D objects. Our framework, Text2Mesh, stylizes a 3D mesh by predicting color and local geometric details which…
Learning Skeletal Articulations with Neural Blend Shapes
Peizhuo Li, Kfir Aberman, Rana Hanocka +3
Animating a newly designed character using motion capture (mocap) data is a long standing problem in computer animation. A key consideration is the skeletal structure that should c…