Peter Fu — accepted AI-conference papers

2026

VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization

Andrei Atanov, Jesse Allardice, Roman Bachmann, Oğuzhan Fatih Kar, R Devon Hjelm, David Griffiths +3

ICML 2026spotlight

Visual tokenizers map high-dimensional raw pixels into a compressed representation for downstream modeling, e.g., conditional video generation. Beyond compression, tokenizers define what information is preserved and how it is organized. A _de facto_ standard approach is to represent a video with a s…

Cited by 3 Source ↗Scholar ↗

2021

ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data

Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Yuri Feigin, Peter Fu, Thomas Gebauer +5

NeurIPS 2021poster

Scene understanding is an active research area. Commercial depth sensors, such as Kinect, have enabled the release of several RGB-D datasets over the past few years which spawned novel methods in 3D scene understanding. More recently with the launch of the LiDAR sensor in Apple’s iPads and iPhones,…

Cited by 206 Source ↗code ↗Scholar ↗