87 citations · 213 across the 18 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…