12 research outputs found
Hamster (Cricetinae) Motion Assisted by Video Tracker Application on Motion Materials in Junior High School
This study aims to explain the phenomenon of motion in hamsters moving in a rolling wheel. Motion is one of the materials taught at the 7th-grade junior high school. The study can be used in learning with the practicum method, namely animal motion. Hamster (Cricetinae) motion is analyzed using a video tracker analysis application with a javascript program. This study used a direct experiment using a hamster using four reference points, namely the forelegs, head, middle body, and hind legs. The results of data analysis from the tracker application were processed using the Microsoft Excel program to obtain a graph of changes in the position and velocity of the hamster. Hamster motion on the rolling wheel is a type of translational motion, namely uniform linear motion. Changes in the hamster's position indicate motion on a horizontal line. The velocity obtained in the tracker application is more accurate than manual calculations
Aggregated Multi-GANs for Controlled 3D Human Motion Prediction
Human motion prediction from historical pose sequence is at the core of many
applications in machine intelligence. However, in current state-of-the-art
methods, the predicted future motion is confined within the same activity. One
can neither generate predictions that differ from the current activity, nor
manipulate the body parts to explore various future possibilities. Undoubtedly,
this greatly limits the usefulness and applicability of motion prediction. In
this paper, we propose a generalization of the human motion prediction task in
which control parameters can be readily incorporated to adjust the forecasted
motion. Our method is compelling in that it enables manipulable motion
prediction across activity types and allows customization of the human movement
in a variety of fine-grained ways. To this aim, a simple yet effective
composite GAN structure, consisting of local GANs for different body parts and
aggregated via a global GAN is presented. The local GANs game in lower
dimensions, while the global GAN adjusts in high dimensional space to avoid
mode collapse. Extensive experiments show that our method outperforms
state-of-the-art. The codes are available at
https://github.com/herolvkd/AM-GAN
Flow-based Autoregressive Structured Prediction of Human Motion
A new method is proposed for human motion predition by learning temporal and
spatial dependencies in an end-to-end deep neural network. The joint
connectivity is explicitly modeled using a novel autoregressive structured
prediction representation based on flow-based generative models. We learn a
latent space of complex body poses in consecutive frames which is conditioned
on the high-dimensional structure input sequence. To construct each latent
variable, the general and local smoothness of the joint positions are
considered in a generative process using conditional normalizing flows. As a
result, all frame-level and joint-level continuities in the sequence are
preserved in the model. This enables us to parameterize the inter-frame and
intra-frame relationships and joint connectivity for robust long-term
predictions as well as short-term prediction. Our experiments on two
challenging benchmark datasets of Human3.6M and AMASS demonstrate that our
proposed method is able to effectively model the sequence information for
motion prediction and outperform other techniques in 42 of the 48 total
experiment scenarios to set a new state-of-the-art
Action2Motion: Conditioned Generation of 3D Human Motions
Action recognition is a relatively established task, where givenan input
sequence of human motion, the goal is to predict its ac-tion category. This
paper, on the other hand, considers a relativelynew problem, which could be
thought of as an inverse of actionrecognition: given a prescribed action type,
we aim to generateplausible human motion sequences in 3D. Importantly, the set
ofgenerated motions are expected to maintain itsdiversityto be ableto explore
the entire action-conditioned motion space; meanwhile,each sampled sequence
faithfully resembles anaturalhuman bodyarticulation dynamics. Motivated by
these objectives, we followthe physics law of human kinematics by adopting the
Lie Algebratheory to represent thenaturalhuman motions; we also propose
atemporal Variational Auto-Encoder (VAE) that encourages adiversesampling of
the motion space. A new 3D human motion dataset, HumanAct12, is also
constructed. Empirical experiments overthree distinct human motion datasets
(including ours) demonstratethe effectiveness of our approach.Comment: 13 pages, ACM MultiMedia 202