PoseVEC: Authoring Adaptive Pose-aware Effects Using Visual Programming and Demonstrations

Document Title

PoseVEC: Authoring Adaptive Pose-aware Effects using Visual Programming and Demonstrations

Document Information

• Subject Area: Human Pose Recognition and Dynamic Effect Generation
• Keywords: Visual Effects, Motion Graphics, Pose Recognition, Programming by Demonstration, Visual Programming

Research Background and Problem

• Problem or Challenge:

  • Current dynamic effect generation (especially pose-based effects) presents technical barriers, particularly for non-technical users.
  • Effects generated using keyframe methods are difficult to adapt to different videos, lack reusability, and have low flexibility.
  • Programming methods require high technical expertise, making them unsuitable for non-technical designers.

• Importance:

  • Pose-based visual effects, such as augmented reality (AR) filters and motion tutorials, are becoming increasingly popular, spanning critical applications in video editing, motion guidance, and personalized dynamic content creation.

• Research Motivation and Related Work:

  • Although existing tools (e.g., Lens Studio, Spark AR) support dynamic effect design, they require significant programming skills.
  • Advanced tools like YouMove and PoseTween still face challenges in reducing development complexity and improving usability for users.

Solution

• Method or Solution:

  • Proposed PoseVEC, a tool for creating pose-based dynamic effects using visual programming and Programming by Demonstration (PbD).
  • Utilizes a node graph model to integrate pose recognition, animation design, and rendering workflows.
  • Provides a real-time interactive user interface, enabling users to define pose-related configurations directly through video canvas interactions.

• Innovations:

  • Combines "visual programming" and "programming by demonstration," simplifying the learning curve for non-technical users through drag-and-drop operations and node connections.
  • Introduces specific designs like "Pose Recognizer Nodes," reducing the need to write low-level pose calculation logic.
  • Supports real-time pose matching using a single pose embedding machine learning model (Pr-VIPE), adaptable to multiple perspectives and motion variations.

• Implementation Steps and Technical Details:

  1. Pose Recognition:
     • Users can select key poses by dragging the video timeline and add corresponding "pose recognition nodes" by clicking.
     • Pose matching is performed using the Pr-VIPE model, with similarity thresholds adjustable.
  2. Animation Design:
     • Supports pose-specific animation parameter generation through data nodes (e.g., joint angles, distance data).
     • Implements dynamic effects like text, GIF animations, and title anchors using "render nodes."
  3. Effect Rendering:
     • Logic nodes control animation trigger conditions (e.g., time-based or sequence-based triggers).
  4. Testing and Optimization:
     • Provides real-time visualization of node data flows, aiding users in understanding and debugging the entire effect workflow.

Research Outcomes

• Specific Outcomes:

  • Developed the PoseVEC tool, enabling users to efficiently create reusable pose-based dynamic effect templates.
  • Provided a workflow model combining "visual programming + video interaction," significantly lowering the technical barriers for dynamic effect creation.

• Advantages Over Existing Solutions:

  • Users can complete the entire process from pose triggering to dynamic effect creation without writing code.
  • Created dynamic effects are reusable and easily adaptable to new videos or real-time camera inputs.
  • Visual debugging features accelerate feedback and optimization during effect design.

• Experimental or Evaluation Results:

  • User Evaluation:
    • Through initial usage studies and demonstration instances, 12 participants (with varying technical backgrounds) successfully created unique pose-based dynamic effects.
    • Approximately 67% of participants expressed a preference for PoseVEC over existing tools.
    • Average user ratings indicated good usability (average 4.33/5) and expressiveness (over 4.42/5).
  • Instance Validation:
    • Successfully reproduced three complex use cases, including interactive motion tutorials, basketball motion visual synchronization, and motion text effects.

• Limitations and Future Directions:

  1. Insufficient Support for Complex Effects:
     • Currently focused on single-person poses, making it difficult to handle multi-person interactions or complex background effects.
  2. UI and Animation Support Improvements:
     • User interface design requires enhancement, such as adding more visual guidance and improving operational flexibility.
  3. Exploration of Other Directions:
     • Develop multimodal sensing tools (e.g., sound or gestures) and expand to depth sensing and multiple scenarios.

Conclusion

PoseVEC provides an innovative, user-friendly approach for non-technical designers, simplifying the creation and reuse of pose-based dynamic visual effects. It holds broad potential in areas such as motion tutorials, editing, and AR content development. Future research can further expand application models, improve user interfaces, and enhance support for complex effects.