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Direct-from-Video: Unsupervised NRSfM

Type of publication Peer-reviewed
Publikationsform Proceedings (peer-reviewed)
Author Lebeda K, Hadfield S, Bowden R,
Project SMILE: Scalable Multimodal sign language Technology for sIgn language Learning and assessmEnt
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Proceedings (peer-reviewed)

Title of proceedings Proceedings of the ECCV workshop on Recovering 6D Object Pose Estimation

Open Access


In this work we describe a novel approach to online dense non-rigid structure from motion. The problem is reformulated, incorporating ideas from visual object tracking, to provide a more general and unified technique, with feedback between the reconstruction and point-tracking algorithms. The resulting algorithm overcomes the limitations of many conventional techniques, such as the need for a reference image/template or precomputed trajectories. The technique can also be applied in traditionally challenging scenarios, such as modelling objects with strong self-occlusions or from an extreme range of viewpoints. The proposed algorithm needs no offline pre-learning and does not assume the modelled object stays rigid at the beginning of the video sequence. Our experiments show that in traditional scenarios, the proposed method can achieve better accuracy than the current state of the art while using less supervision. Additionally we perform reconstructions in challenging new scenarios where state-of-the-art approaches break down and where our method improves performance by up to an order of magnitude.