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A Music-Driven Dance Generation Method Based on a Spatial-Temporal Refinement Model to Optimize Abnormal Frames.

Huaxin Wang1,2,3,4, Yang Song1,2,3,4, Wei Jiang1,2,3,4

  • 1State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China.

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|January 23, 2024
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Summary
This summary is machine-generated.

This study introduces a new spatial-temporal refinement model for music-driven dance generation, significantly improving dance naturalness and realism by optimizing abnormal motion frames. The method enhances consistency between music and dance, resulting in more realistic dance sequences.

Keywords:
abnormal frame optimizationmusic-driven dance generationspatial-temporal refinement model

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Existing music-driven dance generation methods often produce unnatural movements due to abnormal motion frames.
  • This limitation impacts the overall realism and aesthetic quality of generated dance sequences.
  • A need exists for advanced models that can refine and correct these motion anomalies.

Purpose of the Study:

  • To propose a novel music-driven dance generation method utilizing a spatial-temporal refinement model.
  • To optimize abnormal motion frames within dance sequences for enhanced naturalness.
  • To improve the consistency between audio (music) and visual (dance) modalities.

Main Methods:

  • Cross-modal alignment model to learn audio-dance correspondences and match music/dance segments.
  • Abnormal frame optimization algorithm to correct inconsistencies in the dance sequence.
  • Temporal refinement model to ensure synchronization between music beats and dance rhythms.

Main Results:

  • The proposed method successfully generates realistic and natural dance video sequences.
  • Achieved a 1.2 reduction in the Fréchet Inception Distance (FID) index, indicating improved realism.
  • Improved the diversity index by 1.7, suggesting more varied and less repetitive dance movements.

Conclusions:

  • The spatial-temporal refinement model effectively addresses abnormal motion in music-driven dance generation.
  • The method significantly enhances the realism and naturalness of generated dance sequences.
  • This approach offers a promising direction for creating more engaging and believable AI-generated dance.