Improving Viewing Experiences of First-Person Shooter Gameplays with Automatically-Generated Motion Effects

Title of the Paper

Improving Viewing Experiences of First-Person Shooter Gameplays with Automatically-Generated Motion Effects

Paper Information

• Research Area: Human-Computer Interaction (HCI), Multisensory Experience, Esports Viewing Experience
• Keywords: Games, Gameplay Streaming, Viewing Experience, Motion Effects, Automatic Generation, Multisensory Stimulation

Research Background and Problem Statement

• Identified Issues or Challenges:

  1. Esports gameplay streaming is becoming a popular entertainment activity, but optimizing the viewing experience requires further exploration.
  2. Current multisensory experiences are primarily focused on movies and theme parks, with limited application in video game viewing.
  3. Designing multisensory effects manually requires expertise and is costly, whereas automatic generation offers a more feasible solution.

• Significance:

  1. Video game viewing has evolved from a personal activity into a widespread public experience.
  2. Providing an enhanced viewing experience is crucial for attracting audiences and promoting esports.

• Research Motivation and Related Work:

  1. Integrating 4D platforms with esports viewing experiences has significant application potential.
  2. Developing algorithms for automatically generating motion effects offers a cost-effective and scalable solution for improving video game viewing experiences.

Solution

• Methods and Solutions: The authors propose two automatic motion effect generation algorithms specifically designed for first-person shooter (FPS) games:

  1. Motion Generation Algorithm (ME-MOV): Estimates camera motion based on video sequences to generate motion effects representing the movement of the game character.
  2. Gunfire Event Motion Generation Algorithm (ME-GUN): Detects weapon firing events through audio signals and generates motion effects simulating gunfire recoil.

• Innovations:

  1. Utilization of computer vision techniques to estimate camera displacement and rotation within the game footage.
  2. Application of deep neural network models to detect gunfire sounds from the game character and generate custom motion profiles for motion effects.
  3. Combining the two motion effects to provide viewers with a stronger sense of immersion.

• Implementation Steps and Techniques:

  • ME-MOV:
    1. Employ optical flow techniques and the RANSAC algorithm to estimate camera motion (displacement and angular velocity).
    2. Use high-speed filters to generate motion commands for motion chairs.
  • ME-GUN:
    1. Train a DNN classifier using manually labeled game audio data.
    2. Generate motion effects mimicking weapon recoil (primarily for pitch movements of the chair).

Research Outcomes

• Specific Results:

  1. Developed two motion effect generation algorithms and validated their applicability in FPS game viewing.
  2. User studies demonstrated that motion effects significantly enhance the viewing experience, including enjoyment and immersion.

• Advantages:

  1. The automatic motion effect generation method is cost-effective and highly versatile, applicable to any game title.
  2. Provides a faster solution compared to existing manual design methods while maintaining a certain level of quality.

• Experimental and Evaluation Results:

  1. User study results showed that motion effects significantly improved viewers' enjoyment (Q3) and immersion (Q4) compared to no motion effects, without negatively impacting focus (Q1).
  2. Combined motion effects (ME-COM) performed best in terms of user preference, audiovisual matching, and immersion scores.

• Limitations and Future Directions:

  1. The prototype system is designed for motion effects targeting a single FPS character; further research is needed to expand to multiple characters or other game genres.
  2. Prolonged viewing may lead to fatigue, necessitating the development of mechanisms to control motion effect intensity.
  3. The sound detection model requires optimization to support real-time data processing and address potential asynchronous issues in live streaming.
  4. Consider adding motion effects corresponding to more actions (e.g., jumping or being attacked) to further enrich the viewing experience.

Conclusion

This study proposes an innovative method to enhance the viewing experience of first-person shooter games by introducing two motion effect generation algorithms based on video and audio streams. The approach significantly improves audience immersion and enjoyment. The research strengthens the practical potential of enhancing esports viewing experiences and provides a solid foundation for future multisensory esports viewing innovations.