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INR Smooth: Interframe noise relation-based smooth video synthesis on diffusion models.

Cuihong Yu1, Cheng Han1, Chao Zhang1

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.

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|April 29, 2025
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Summary
This summary is machine-generated.

This study introduces INR Smooth, a novel video smoothing strategy that enhances text-to-video (T2V) generation by addressing interframe inconsistencies. The method improves temporal consistency and text alignment without additional computational resources.

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Machine Learning

Background:

  • Text-to-video (T2V) generation faces challenges like frame inconsistency and poor text alignment, hindering video smoothness.
  • Existing smoothing methods often sacrifice background texture and artistic expression.

Purpose of the Study:

  • To propose INR Smooth, a video smoothing strategy addressing interframe noise relationships for improved T2V generation.
  • To develop training-based and training-free methods for video smoothing editing.

Main Methods:

  • INR Smooth strategy based on interframe noise relationships.
  • Training-based method: simultaneous noise constraints and smoothing loss functions.
  • Training-free method: DDIM Inversion for enhanced text alignment.

Main Results:

  • Significant improvements in text alignment and temporal consistency.
  • Outstanding performance in smooth transitions and artistic style portrayal.
  • Training-free and zero-shot fine-tuning methods require no additional computing resources.

Conclusions:

  • INR Smooth effectively enhances video smoothing in T2V tasks.
  • The proposed methods improve both visual quality and adherence to text prompts.
  • Accessible implementation with provided source code and demos.