54,582 research outputs found
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
Executing your Commands via Motion Diffusion in Latent Space
We study a challenging task, conditional human motion generation, which
produces plausible human motion sequences according to various conditional
inputs, such as action classes or textual descriptors. Since human motions are
highly diverse and have a property of quite different distribution from
conditional modalities, such as textual descriptors in natural languages, it is
hard to learn a probabilistic mapping from the desired conditional modality to
the human motion sequences. Besides, the raw motion data from the motion
capture system might be redundant in sequences and contain noises; directly
modeling the joint distribution over the raw motion sequences and conditional
modalities would need a heavy computational overhead and might result in
artifacts introduced by the captured noises. To learn a better representation
of the various human motion sequences, we first design a powerful Variational
AutoEncoder (VAE) and arrive at a representative and low-dimensional latent
code for a human motion sequence. Then, instead of using a diffusion model to
establish the connections between the raw motion sequences and the conditional
inputs, we perform a diffusion process on the motion latent space. Our proposed
Motion Latent-based Diffusion model (MLD) could produce vivid motion sequences
conforming to the given conditional inputs and substantially reduce the
computational overhead in both the training and inference stages. Extensive
experiments on various human motion generation tasks demonstrate that our MLD
achieves significant improvements over the state-of-the-art methods among
extensive human motion generation tasks, with two orders of magnitude faster
than previous diffusion models on raw motion sequences.Comment: 18 pages, 11 figures, conferenc
Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications
Developing an intelligent vehicle which can perform human-like actions
requires the ability to learn basic driving skills from a large amount of
naturalistic driving data. The algorithms will become efficient if we could
decompose the complex driving tasks into motion primitives which represent the
elementary compositions of driving skills. Therefore, the purpose of this paper
is to segment unlabeled trajectory data into a library of motion primitives. By
applying a probabilistic inference based on an iterative
Expectation-Maximization algorithm, our method segments the collected
trajectories while learning a set of motion primitives represented by the
dynamic movement primitives. The proposed method utilizes the mutual
dependencies between the segmentation and representation of motion primitives
and the driving-specific based initial segmentation. By utilizing this mutual
dependency and the initial condition, this paper presents how we can enhance
the performance of both the segmentation and the motion primitive library
establishment. We also evaluate the applicability of the primitive
representation method to imitation learning and motion planning algorithms. The
model is trained and validated by using the driving data collected from the
Beijing Institute of Technology intelligent vehicle platform. The results show
that the proposed approach can find the proper segmentation and establish the
motion primitive library simultaneously
Negotiating the Probabilistic Satisfaction of Temporal Logic Motion Specifications
We propose a human-supervised control synthesis method for a stochastic
Dubins vehicle such that the probability of satisfying a specification given as
a formula in a fragment of Probabilistic Computational Tree Logic (PCTL) over a
set of environmental properties is maximized. Under some mild assumptions, we
construct a finite approximation for the motion of the vehicle in the form of a
tree-structured Markov Decision Process (MDP). We introduce an efficient
algorithm, which exploits the tree structure of the MDP, for synthesizing a
control policy that maximizes the probability of satisfaction. For the proposed
PCTL fragment, we define the specification update rules that guarantee the
increase (or decrease) of the satisfaction probability. We introduce an
incremental algorithm for synthesizing an updated MDP control policy that
reuses the initial solution. The initial specification can be updated, using
the rules, until the supervisor is satisfied with both the updated
specification and the corresponding satisfaction probability. We propose an
offline and an online application of this method.Comment: 9 pages, 4 figures; The results in this paper were presented without
proofs in IEEE/RSJ International Conference on Intelligent Robots and Systems
November 3-7, 2013 at Tokyo Big Sight, Japa
Branching diffusion representation of semi-linear elliptic PDEs and estimation using Monte Carlo method
We study semi-linear elliptic PDEs with polynomial non-linearity and provide
a probabilistic representation of their solution using branching diffusion
processes. When the non-linearity involves the unknown function but not its
derivatives, we extend previous results in the literature by showing that our
probabilistic representation provides a solution to the PDE without assuming
its existence. In the general case, we derive a new representation of the
solution by using marked branching diffusion processes and automatic
differentiation formulas to account for the non-linear gradient term. In both
cases, we develop new theoretical tools to provide explicit sufficient
conditions under which our probabilistic representations hold. As an
application, we consider several examples including multi-dimensional
semi-linear elliptic PDEs and estimate their solution by using the Monte Carlo
method
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