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Learning joint reconstruction of hands and manipulated objects


Conference Paper


Estimating hand-object manipulations is essential for interpreting and imitating human actions. Previous work has made significant progress towards reconstruction of hand poses and object shapes in isolation. Yet, reconstructing hands and objects during manipulation is a more challenging task due to significant occlusions of both the hand and object. While presenting challenges, manipulations may also simplify the problem since the physics of contact restricts the space of valid hand-object configurations. For example, during manipulation, the hand and object should be in contact but not interpenetrate. In this work, we regularize the joint reconstruction of hands and objects with manipulation constraints. We present an end-to-end learnable model that exploits a novel contact loss that favors physically plausible hand-object constellations. Our approach improves grasp quality metrics over baselines, using RGB images as input. To train and evaluate the model, we also propose a new large-scale synthetic dataset, ObMan, with hand-object manipulations. We demonstrate the transferability of ObMan-trained models to real data.

Author(s): Yana Hasson and Gül Varol and Dimitrios Tzionas and Igor Kalevatykh and Michael J. Black and Ivan Laptev and Cordelia Schmid
Book Title: Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)
Year: 2019
Month: June

Department(s): Perceiving Systems
Research Project(s): Hands in Action
Learning from Synthetic Data
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Event Name: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019
Event Place: Long Beach, USA

URL: https://www.di.ens.fr/willow/research/obman
Attachments: pdf


  title = {Learning joint reconstruction of hands and manipulated objects},
  author = {Hasson, Yana and Varol, G{\"u}l and Tzionas, Dimitrios and Kalevatykh, Igor and Black, Michael J. and Laptev, Ivan and Schmid, Cordelia},
  booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  month = jun,
  year = {2019},
  url = {https://www.di.ens.fr/willow/research/obman},
  month_numeric = {6}