Ikea Furniture Assembly Dataset

Ikea Furniture Assembly Dataset (IkeaFA) is a video dataset. Each video contains a single actor assembling and disassembling an Ikea furniture. The dataset could be helpful for researches on compositional actions and long-term video understanding.

  • Size: 101 videos, 1920x1080, 30fps, each 2-4 minutes long.
  • Camera control: all videos are captured by a stationary GoPro camera.
  • Scene control: furniture assembly task is performed either on a table or on the floor.
  • Variance control: 14 actors with different assembly styles and clothing.
  • Classes: we define 12 action classes including pick leg, attach leg 1/2/3/4, detach leg 1/2/3/4, spin in/out, flip table. Also a null action is assigned to actions that don’t belong to any listed actions above.
  • Annotations: We provide two sets of annotations, see the README.txt for details
    1. temporal time stamp for each action
    2. bounding box for the Ikea table

Sample videos

On the table On the floor


You can download the dataset [here] (9.5GB).

The zip file has following structure:

├── README.txt
├── IkeaClipsDB.mat
├── videos
    ├── 2016-08-11
        ├── GOPR*.MP4
    ├── 2016-08-18
        ├── GOPR*.MP4
    ├── 2016-09-01
        ├── GOPR*.MP4
├── processed-python-data
    ├── ikea_action_data.h5


  • [1] Human Action Forecasting by Learning Task Grammars. Tengda Han, Jue Wang, Anoop Cherian, and Stephen Gould (2017). Tech report. [paper]

  • [2] Human Pose Forecasting via Deep Markov Models. Sam Toyer, Anoop Cherian, Tengda Han, Stephen Gould (2017). DICTA 2017. [paper]

[1] Han et al. [2] Toyer et al.
  • If you find this dataset useful and use it in published work, please remember to cite the following two papers
    title={Human Action Forecasting by Learning Task Grammars},
    author={Han, Tengda and Wang, Jue and Cherian, Anoop and Gould, Stephen},

    title={Human Pose Forecasting via Deep {M}arkov Models},
    author={Toyer, Sam and Cherian, Anoop and Han, Tengda and Gould, Stephen},


  • We highly appreciate the contribution of Andy, Basura, Daniel, Elaine, Guofeng, Han, Harris, Jay, Jue, Kitt, Mike, Rodrigo, Sam and Xinyu for participating in our data collection effort.

  • We thank ANU and ACRV for the support of GPUs and furnitures.

  • Website updated at March 2018.

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