GAN-Poser: an improvised bidirectional GAN model for human motion prediction

Abstract

A novel method called GAN-Poser has been explored to predict human motion in less time given an input 3D human skeleton sequence based on a generator–discriminator framework. Specifically, rather than using the conventional Euclidean loss, a frame-wise geodesic loss is used for geometrically meaningful and more precise distance measurement. In this paper, we have used a bidirectional GAN framework along with a recursive prediction strategy to avoid mode-collapse and to further regularize the training. To be able to generate multiple probable human-pose sequences conditioned on a given starting sequence, a random extrinsic factor $$\varTheta$$ has also been introduced. The discriminator is trained in order to regress the extrinsic factor $$\varTheta$$ , which is used alongside with the intrinsic factor (encoded starting pose sequence) to generate a particular pose sequence. In spite of being in a probabilistic framework, the modified discriminator architecture allows predictions of an intermediate part of pose sequence to be used as conditioning for prediction of the latter part of the sequence. This adversarial learning-based model takes into consideration of the stochasticity, and the bidirectional setup provides a new direction to evaluate the prediction quality against a given test sequence. Our resulting novel method, GAN-Poser, achieves superior performance over the state-of-the-art deep learning approaches when evaluated on the standard NTU-RGB-D and Human3.6 M dataset.