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ASMNet: Action and Style-Conditioned Motion Generative Network for 3D Human Motion Generation.

Zongying Li1, Yong Wang1, Xin Du1

  • 1School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China.

Cyborg and Bionic Systems (Washington, D.C.)
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Summary

This study introduces ASMNet, a novel network for generating human motion with specific actions and styles. ASMNet effectively captures motion features and injects style, outperforming existing methods.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human Motion Analysis

Background:

  • Human motion generation research faces challenges with style diversity and limited style-specific data.
  • Different emotional states (e.g., joy, sorrow) significantly alter human motion, yet capturing these nuances is difficult.

Purpose of the Study:

  • To propose ASMNet (Action and Style-conditioned Motion generative Network) for generating human motion sequences that adhere to both action labels and stylistic features.
  • To address the limitations in style-conditioned motion data by developing a robust generative model.

Main Methods:

  • Designed a spatial-temporal extractor to effectively capture motion features from human motion sequences.
  • Utilized adaptive instance normalization layers to inject desired style information into the generated motion.
  • Developed an action and style-conditioned generative network (ASMNet).

Main Results:

  • Generated human motion sequences demonstrated compliance with specified action labels and stylistic attributes.
  • ASMNet achieved results comparable to state-of-the-art methods.
  • Substantial quantitative and qualitative advantages were observed in the evaluation of generated motions.

Conclusions:

  • ASMNet successfully generates human motion with both action and style control, overcoming data limitations.
  • The proposed spatial-temporal extractor and adaptive instance normalization are effective for style injection.
  • The model shows significant potential for applications requiring nuanced and stylistically accurate human motion generation.