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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.
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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...
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FCMD: Fine-Grained Text-Driven Cohesive Motion Generation With Diffusion Model.

Shuai Li, Siqi Wang, Xinyu Zhang

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    Summary
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    FCMD, a new diffusion model, generates coherent human motion from text. It improves realism and transitions using fine-grained text fusion and history motion guidance for better gaming and film applications.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Human Motion Generation

    Background:

    • Generating realistic human motion from text is vital for digital applications like gaming and filmmaking.
    • Current methods face challenges in maintaining global coherence, frame continuity, and smooth transitions.

    Purpose of the Study:

    • To introduce FCMD, a novel diffusion-based model for generating cohesive human motion sequences from fine-grained textual descriptions.
    • To address limitations in existing methods regarding motion coherence, continuity, and transitions.

    Main Methods:

    • FCMD utilizes Fine-grained Text Fusion to integrate detailed text with transitional narratives for semantic consistency.
    • History Motion Guidance ensures accuracy and consistency across consecutive frames.
    • Smooth Stitching Sampling leverages preceding and current motion data for seamless transitions.
    • A large language model (LLM) is employed to refine motion datasets by extracting fine-grained textual descriptions.

    Main Results:

    • FCMD demonstrates superior performance compared to state-of-the-art methods.
    • The model generates coherent, natural, and highly controllable motion sequences.
    • Experimental results validate the effectiveness of the proposed innovations.

    Conclusions:

    • FCMD offers a significant advancement in text-to-motion generation.
    • The model's approach enhances semantic consistency, frame accuracy, and transition smoothness.
    • FCMD provides a robust solution for creating realistic human motion in various applications.