Video Domain Adaptation based on Optimal Transport in Grassmann Manifolds

Long, Tianhang; Sun, Yanfeng; Gao, Junbin; Hu, Yongli; Yin, Baocai

doi:10.1016/j.ins.2022.01.044

Cited by 8 publications

(1 citation statement)

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“…[11] has shown an unsupervised approach for images. Recent studies extend this approach also to video data [12] and to acoustic data [13]. Generative approaches focus on generating new data in the target domain from a given source domain.…”

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VoloGAN: Adversarial Domain Adaptation for Synthetic Depth Data

Kirch¹,

Pagés²,

Arnaldo³

et al. 2022

Preprint

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We present VoloGAN, an adversarial domain adaptation network that translates synthetic RGB-D images of a high-quality 3D model of a person, into RGB-D images that could be generated with a consumer depth sensor. This system is especially useful to generate high amount training data for single-view 3D reconstruction algorithms replicating the real-world capture conditions, being able to imitate the style of different sensor types, for the same high-end 3D model database. The network uses a CycleGAN framework with a U-Net architecture for the generator and a discriminator inspired by SIV-GAN. We use different optimizers and learning rate schedules to train the generator and the discriminator. We further construct a loss function that considers image channels individually and, among other metrics, evaluates the structural similarity. We demonstrate that CycleGANs can be used to apply adversarial domain adaptation of synthetic 3D data to train a volumetric video generator model having only few training samples.

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confidence: 99%