Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

362
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...
362
Diffusion01:12

Diffusion

192.3K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
192.3K
Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

446
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...
446
Kinematic Equations - III01:18

Kinematic Equations - III

7.6K
The first two kinematic equations have time as a variable, but the third kinematic equation is independent of time. This equation expresses final velocity as a function of the acceleration and distance over which it acts. The fourth kinematic equation does not have an acceleration term and provides the final position of the object at time t in terms of the initial and final velocities. This equation is useful when the value of the constant acceleration is unknown.
Using the kinematic equations,...
7.6K
Kinematic Equations - II01:17

Kinematic Equations - II

9.5K
The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
9.5K
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

359
A slider-crank mechanism 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. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
359

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

LN3Diff++: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Compositional Generative Model of Unbounded 4D Cities.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

DiffTF++: 3D-Aware Diffusion Transformer for Large-Vocabulary 3D Generation.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Unified 3D and 4D Panoptic Segmentation via Dynamic Shifting Networks.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-Based Perception.

IEEE transactions on pattern analysis and machine intelligence·2021
Same author

Risk factors of low back pain among the Chinese occupational population: a case-control study.

Biomedical and environmental sciences : BES·2012
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jul 4, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

9.7K

运动扩散:以文本驱动的人类运动生成与扩散模型.

Mingyuan Zhang, Zhongang Cai, Liang Pan

    IEEE transactions on pattern analysis and machine intelligence
    |January 29, 2024
    PubMed
    概括
    此摘要是机器生成的。

    MotionDiffuse是一种新的扩散模型,可以从文本中生成多样化和细粒度的人类运动. 这个框架克服了当前基于文本的动作合成的局限性,提供了现实的和可控的结果.

    更多相关视频

    A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA
    12:05

    A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA

    Published on: October 1, 2017

    8.2K
    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
    00:10

    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

    Published on: September 5, 2019

    8.2K

    相关实验视频

    Last Updated: Jul 4, 2025

    Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
    09:32

    Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

    Published on: April 11, 2018

    9.7K
    A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA
    12:05

    A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA

    Published on: October 1, 2017

    8.2K
    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
    00:10

    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

    Published on: September 5, 2019

    8.2K

    科学领域:

    • 计算机图形 计算机图形
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 人类运动建模对于图形应用至关重要,但需要专门的技能.
    • 现有的以文本驱动的运动生成方法在多样性和细粒度控制方面扎.
    • 弥合非专业人士在运动生成方面的技能差距是一个持续的挑战.

    研究的目的:

    • 推出MotionDiffuse,一个基于扩散模型的框架,用于以文本为驱动的人类运动生成.
    • 从自然语言中生成人类动作的多样性,现实主义和可控性.
    • 解决目前运动生成技术中确定性映射的局限性.

    主要方法:

    • 开发了MotionDiffuse,这是一个使用扩散模型进行概率文本运动映射的框架.
    • 采用了一系列无声化的步骤来注入变化并产生多样化的运动.
    • 实现多层次的操纵,以精确控制身体部位和运动长度.

    主要成果:

    • 在以文本驱动和动作为条件的动作生成中,MotionDiffuse在最先进的方法中表现出优越的性能.
    • 该框架擅长模拟复杂的数据分布,从而产生现实和生动的动作序列.
    • 定性分析证实了MotionDiffuse对运动生成任务的全面可控性.

    结论:

    • MotionDiffuse在以文本为驱动的人类运动生成方面取得了重大进展.
    • 概率的方法和多层次的操纵能力提供了增强的现实主义和控制.
    • 这种框架有效地降低了使用自然语言创建复杂的人类运动的技能障碍.