SPORT: From Zero-Shot Prompts to Real-Time Motion Generation

Summary

This summary is machine-generated.

SPORT generates real-time, diverse motions from text prompts using a novel framework. This approach enhances motion quality and diversity for applications in computer animation and gaming.

Area Of Science

  • Computer Graphics
  • Artificial Intelligence

Background

  • Real-time motion generation is crucial for animation and gaming.
  • Current methods produce limited, short motion clips due to isolated style/content controls.

Purpose Of The Study

  • Introduce SPORT (from zero-Shot Prompt tO Real-Time motion generation) for dynamic, real-time motion synthesis.
  • Address the semantic gap between abstract text prompts and motion generation.
  • Improve motion quality, diversity, and inference speed.

Main Methods

  • SPORT framework with three components: body-part phase autoencoder, body-part content encoder, and diffusion-based decoder.
  • Zero-shot prompting for flexible motion control.
  • Unity prototype for real-time application demonstration.

Main Results

  • SPORT effectively bridges the semantic gap for abstract text and dynamic terrains.
  • Demonstrated superior performance over existing methods in motion quality, style diversity, and inference speed.
  • Successful real-time motion generation prototype developed.

Conclusions

  • SPORT offers a robust solution for real-time, diverse motion generation.
  • The framework enhances realism and adaptability in animated content.
  • Enables more sophisticated and responsive character behaviors in virtual environments.

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