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Make-Your-Video: Customized Video Generation Using Textual and Structural Guidance.

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    This summary is machine-generated.

    Make-Your-Video generates customized videos using text descriptions and motion structure. This novel approach enhances temporal coherence and user control in text-to-video synthesis.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Text-to-video synthesis aims to create videos from textual prompts.
    • Current methods often lack precise control over video content and motion.

    Purpose of the Study:

    • To develop a customized video generation method using text for context and motion structure for guidance.
    • To improve temporal coherence and fidelity to user guidance in synthesized videos.

    Main Methods:

    • Utilized a Latent Diffusion Model pre-trained on still images, enhanced with temporal modules for video generation.
    • Implemented a two-stage learning scheme to leverage image dataset knowledge for video synthesis.
    • Employed a causal attention mask strategy for extended video generation and quality maintenance.

    Main Results:

    • Demonstrated superior performance over existing methods in temporal coherence and adherence to user guidance.
    • The two-stage learning scheme reduced computational resource requirements.
    • The causal attention mask effectively mitigated quality degradation in longer videos.

    Conclusions:

    • The Make-Your-Video method offers enhanced control and coherence for text-to-video generation.
    • The approach shows potential for practical applications in customized video creation.