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Planar Rigid-Body Motion

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Related Experiment Video

Updated: Jun 6, 2026

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment
06:25

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Published on: December 23, 2020

Motion imitation with a handheld camera.

Guofeng Zhang1, Hanqing Jiang, Jin Huang

  • 1State Key Laboratory of CAD&CG, Zijingang Campus, Zhejiang University, Hangzhou 310058, PR China. zhangguofeng@cad.zju.edu.cn

IEEE Transactions on Visualization and Computer Graphics
|December 15, 2010
PubMed
Summary

This study introduces a new method for extracting object motion from handheld videos to animate 3D characters, automating motion imitation without special equipment.

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Area of Science:

  • Computer Vision
  • Computer Graphics
  • Animation

Background:

  • Traditional motion capture requires specialized equipment and controlled environments.
  • Manual keyframing by animators is time-consuming and labor-intensive.
  • There is a need for accessible methods to generate 3D character motion from readily available video data.

Purpose of the Study:

  • To present a novel method for extracting dynamic object motion from handheld videos.
  • To automate the process of motion imitation for 3D characters.
  • To increase the supply of motion data by utilizing casual videos.

Main Methods:

  • Tracking 2D and 3D trajectories of a dynamic reference object in video sequences.
  • Solving an optimization problem to transfer extracted motion to a target 3D character, preserving motion gradients.
  • Developing a user-friendly editing environment for fine-tuning motion details.

Main Results:

  • Successfully extracted and transferred motion from various dynamic objects (e.g., animals) to 3D characters.
  • Demonstrated automation of motion imitation, reducing reliance on manual keyframing.
  • Enabled the use of casual videos as a source for motion data.

Conclusions:

  • The proposed method offers an efficient and accessible approach to 3D character motion synthesis.
  • It significantly simplifies the process of acquiring motion data from real-world videos.
  • The system has the potential to democratize 3D animation by lowering technical barriers.