Weifeng Chen

I work on GenAI at Meta. I did my Ph.D. at the University of Michigan, Ann Arbor advised by Prof. Jia Deng in the Princeton Vision & Learning Lab.

wfchen@umich.edu  /  Google Scholar  /  Github

We aim to enhance the generalization capability of affordance grounding to in-the-wild objects that are unseen during training, by developing a new approach AffordanceLLM, that takes the advantage of the rich knowledge from large-scale VLMs.

We propose a semi-supervised approach for contemporary object detectors following the teacher-student dual model framework.

We propose a hierarchical reinforcement framework to learn high-level interactive tasks.

We present Open Annotations of Single Image Surfaces (OASIS), a dataset consisting of detailed 3D geometry for images in the wild.

We propose a method to automatically generate training data for single-view depth through Structure-from-Motion (SfM) on Internet videos.

We present a new dataset "Surface Normals in the Wild" consisting of images in the wild annotated with surface normals of random points.

We introduce a new dataset "Depth in the Wild" consisting of images in the wild annotated with relative depth between pairs of random points.

We design a mesh-based framework for creating panoramas from wide-baseline images.

We align CAD models interactively to fix artifacts in MVS output.

Reviewer: CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, SIGGRAPH Asia, ICRA, AAAI, WACV, ACCV