4 citations · 7 across the 10 of their papers we have counts for
7 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…
Best Segmentation Buddies for Image-Shape Correspondence
Itai Lang, Dongwei Lyu, Dale Decatur +1
Finding correspondences is a fundamental and extensively researched problem in computer vision and graphics. In this work, we examine the underexplored task of estimating segmentat…
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…
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…