From the 1 of 12 linked papers with an AI index.
6 papers · 1 filter
MeshFM: 2D Features Are All You Need for 3D Shape Understanding
Jinfan Zhou, Richard Liu, Itai Lang +1
MeshFM is a feedforward framework that learns 3D shape features by distilling 2D features from visual foundation models using a two‑stage training process that requires no 3D annot…
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…
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…
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…