Generative Adversarial Network for Future Hand Segmentation from Egocentric Video

Wenqi Jia, Miao Liu, James M. Rehg

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We introduce the novel problem of anticipating a time series of future hand masks from egocentric video. A key challenge is to model the stochasticity of future head motions, which globally impact the head-worn camera video analysis. To this end, we propose a novel deep generative model – EgoGAN. Our model first utilizes a 3D Fully Convolutional Network to learn a spatio-temporal video representation for pixel-wise visual anticipation. It then generates future head motion using the Generative Adversarial Network (GAN), and predicts the future hand masks based on both the encoded video representation and the generated future head motion. We evaluate our method on both the EPIC-Kitchens and the EGTEA Gaze+ datasets. We conduct detailed ablation studies to validate the design choices of our approach. Furthermore, we compare our method with previous state-of-the-art methods on future image segmentation and provide extensive analysis to show that our method can more accurately predict future hand masks. Project page:

Original languageEnglish (US)
Title of host publicationComputer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings
EditorsShai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner
Number of pages18
ISBN (Print)9783031197772
StatePublished - 2022
Externally publishedYes
Event17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel
Duration: Oct 23 2022Oct 27 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13673 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th European Conference on Computer Vision, ECCV 2022
CityTel Aviv


  • Egocentric vision
  • Hand segmentation
  • Visual anticipantation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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