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HP-GAN: Probabilistic 3D human motion prediction via GAN
Predicting and understanding human motion dynamics has many applications,
such as motion synthesis, augmented reality, security, and autonomous vehicles.
Due to the recent success of generative adversarial networks (GAN), there has
been much interest in probabilistic estimation and synthetic data generation
using deep neural network architectures and learning algorithms.
We propose a novel sequence-to-sequence model for probabilistic human motion
prediction, trained with a modified version of improved Wasserstein generative
adversarial networks (WGAN-GP), in which we use a custom loss function designed
for human motion prediction. Our model, which we call HP-GAN, learns a
probability density function of future human poses conditioned on previous
poses. It predicts multiple sequences of possible future human poses, each from
the same input sequence but a different vector z drawn from a random
distribution. Furthermore, to quantify the quality of the non-deterministic
predictions, we simultaneously train a motion-quality-assessment model that
learns the probability that a given skeleton sequence is a real human motion.
We test our algorithm on two of the largest skeleton datasets: NTURGB-D and
Human3.6M. We train our model on both single and multiple action types. Its
predictive power for long-term motion estimation is demonstrated by generating
multiple plausible futures of more than 30 frames from just 10 frames of input.
We show that most sequences generated from the same input have more than 50\%
probabilities of being judged as a real human sequence. We will release all the
code used in this paper to Github
Skeleton-aided Articulated Motion Generation
This work make the first attempt to generate articulated human motion
sequence from a single image. On the one hand, we utilize paired inputs
including human skeleton information as motion embedding and a single human
image as appearance reference, to generate novel motion frames, based on the
conditional GAN infrastructure. On the other hand, a triplet loss is employed
to pursue appearance-smoothness between consecutive frames. As the proposed
framework is capable of jointly exploiting the image appearance space and
articulated/kinematic motion space, it generates realistic articulated motion
sequence, in contrast to most previous video generation methods which yield
blurred motion effects. We test our model on two human action datasets
including KTH and Human3.6M, and the proposed framework generates very
promising results on both datasets.Comment: ACM MM 201
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