Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

975
Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
Planar motion is typically divided into three distinct categories. The first is rectilinear translation, demonstrated by a subway train that moves along...
975
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

879
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
879
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

702
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
702
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

1.1K
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
1.1K
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

536
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
536
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

691
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
691

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Multimodal deep learning model for AI-based functional prognostic risk stratification in patients undergoing radical nephrectomy.

Nature communications·2026
Same author

Dietary index for gut microbiota (DI-GM) and irritable bowel syndrome: a case-control study.

Scientific reports·2026
Same author

Platelet Hyperactivation Plays a Critical Role in Exacerbating Skin Lesions in Rats with Psoriasis and Blood Stasis Syndrome.

Journal of inflammation research·2025
Same author

DoodleAssist: Progressive Interactive Line Art Generation With Latent Distribution Alignment.

IEEE transactions on visualization and computer graphics·2025
Same author

A rapid and sensitive UHPLC-MS/MS method for epalrestat detection in micro-volumes of human plasma for the first time.

Bioanalysis·2025
Same author

Tape-Casting Fabrication Techniques for Garnet-Based Membranes in Solid-State Lithium-Metal Batteries: A Comprehensive Review.

ACS applied materials & interfaces·2024

Related Experiment Video

Updated: Jan 15, 2026

Computer-Generated Animal Model Stimuli
26:43

Computer-Generated Animal Model Stimuli

Published on: July 29, 2007

11.3K

DiFusion: Flexible Stylized Motion Generation Using Digest-and-Fusion Scheme.

Yatian Wang, Haoran Mo, Chengying Gao

    IEEE Transactions on Visualization and Computer Graphics
    |October 13, 2025
    PubMed
    Summary

    DiFusion generates diverse human motion with text control and style adaptation using a dual-condition diffusion model. This framework enhances realism and diversity in motion synthesis, enabling applications like style interpolation.

    More Related Videos

    FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis
    10:02

    FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

    Published on: December 24, 2014

    12.1K
    Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
    06:55

    Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

    Published on: September 26, 2016

    8.4K

    Related Experiment Videos

    Last Updated: Jan 15, 2026

    Computer-Generated Animal Model Stimuli
    26:43

    Computer-Generated Animal Model Stimuli

    Published on: July 29, 2007

    11.3K
    FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis
    10:02

    FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

    Published on: December 24, 2014

    12.1K
    Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
    06:55

    Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

    Published on: September 26, 2016

    8.4K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Existing text-driven human motion synthesis methods struggle with style expression.
    • Controlling motion style through diverse modalities remains a challenge.

    Purpose of the Study:

    • To propose DiFusion, a novel framework for diversely stylized human motion generation.
    • To enable flexible control over motion content via text and style via multiple modalities (textual labels or motion sequences).

    Main Methods:

    • Employs a dual-condition motion latent diffusion model for independent content and style control.
    • Introduces the Digest-and-Fusion training scheme to handle imbalanced dataset complexities.
    • Digests domain-specific knowledge and adaptively fuses it for compatible training.

    Main Results:

    • Demonstrates superior performance over existing approaches in content alignment, style expressiveness, realism, and diversity.
    • Comprehensive evaluations validate the effectiveness of the DiFusion framework.
    • The method shows potential for practical applications like motion style interpolation.

    Conclusions:

    • DiFusion offers a robust solution for stylized human motion synthesis with flexible multi-modal style control.
    • The Digest-and-Fusion training scheme effectively addresses dataset imbalances.
    • The framework significantly advances the state-of-the-art in human motion generation.