Generalizable 3D part segmentation is important but challenging in vision and robotics.
Training deep models via conventional supervised methods requires large-scale 3D …

Current top-leading solutions for video object segmentation (VOS) typically follow a
matching-based regime: for each query frame, the segmentation mask is inferred according …

Text-to-image diffusion models are now capable of generating images that are often
indistinguishable from real images. To generate such images, these models must …

We present Diff3F as a simple robust and class-agnostic feature descriptor that can be
computed for untextured input shapes (meshes or point clouds). Our method distills diffusion …

CAD programs are a popular way to compactly encode shapes as a sequence of operations
that are easy to parametrically modify. However without sufficient semantic comments and …

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 …

The past few years have witnessed the great success and prevalence of self-supervised
representation learning within the language and 2D vision communities. However, such …

Objectives: We focus on exploring an unsupervised learning-based model which can take
advantage of a single image and events to estimate dense and time-continuous optical flow …

We present the first active learning tool for fine-grained 3D part labeling, a problem which
challenges even the most advanced deep learning (DL) methods due to the significant …

3D object part segmentation is essential in computer vision applications. While substantial
progress has been made in 2D object part segmentation, the 3D counterpart has received …