Matte Painting and First-Person View Drones: History
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Contributor:
Chao Gu
,
Jie Sun
,
Tong Chen
,
Wei Miao
,
Yunshuo Yang
,
Shuyuan Lin
,
Jiangjie Chen

In terms of the teaching process of matte painting, it is essential for students to develop a sound understanding of the relationship between virtual and physical environments. First-person view (FPV) drones are applied to matte painting courses. This provides students with a better learning environment using a digital education system. It indicates that the flow experience, learning interest, and continuous learning intention of students who use FPV drones in matte painting are significantly greater than those of students who only utilize traditional teaching methods.

  • matte painting
  • first-person view drones
  • interaction learning
  • continuous learning intention

1. Introduction

The film industry is closely related to technology. The development of industry trends and the improvement of audience aesthetics have led to an increasing number of filmmakers using digital special effects in their films. Digital special effects help films tell stories more effectively through technology innovation (Tong et al. 2021). The visual effects have a significant influence on the viewers’ evaluation of the film. With the aid of technology, movies can directly bring to the audience a sense of beauty by using visual effects (Bramley et al. 2018). Matte painting plays an important role in post-production special effects in film production (Ramdhan and Nugraha 2020). This method allows the addition of a virtual scene to an actual filmed scene, and thus enhances the visual impact of the film. The development of the technology in film and television has made matte painting one of the most important required courses for college students in the design field.
Matte painting courses may be improved by using interactive learning methods. There have been several studies that examine the benefits of interactive design applications in education. For instance, a spherical video-based immersive virtual reality learning system is used in courses regarding landscape design. Students benefit from this technique by generating more positive learning achievements, learning attitude, and self-regulation (Wu et al. 2021). In addition to the learning environment, interactive learning cases may also contribute to the overall learning experience. Utilizing augmented reality in basic design courses to assist students in understanding the shape of objects can create positive flow experiences and continuous learning intention (Hsu et al. 2021), and utilizing eye-tracking games can enhance students’ interest in learning about game design (Gu et al. 2022a). The use of innovative methods of human-computer interaction (HCI) can enhance the learning experience. When students participate in interactive learning, students’ desire for continuous learning increases (Chen et al. 2021).
With first-person view (FPV) drones, traditional classrooms are no longer bound by physical limitations, and can benefit from improved two-way communication between teachers and students. An FPV drone can convert photos into 3D models to aid students in visualizing the area of interest (Bolick et al. 2022). In addition, using the real-time footage of the FPV drones, students gain a different perspective from real-world experience in the matte painting course. And this perspective happens to be similar to that of a matte painting or modeling program. Even though interactive learning has attracted a lot of attention from researchers, there is currently no study evaluating the effects of FPV drones on the teaching of matte painting courses. Matte painting is a post-production course for film and television which teaches students to depict scenes virtually in a near-real-life manner. According to the matte painting syllabus, a primary focus of the course is effectively improving the perception of the real environment by the students. Developing this ability will help students draw more realistic and vivid virtual scenes.

2. FPV Drones Applied to Matte Painting Courses

Drones operation modes include first-person and third-person view. When using the third-person view, the user can observe the drone’s overall flight and the relationship between it and the surrounding environment. On the other hand, when using the first-person view to maneuver the drone, users are able to experience the sensation of flying a drone from the drone cockpit. There are different technical solutions for realizing control of FPV drones. For example, drones can be attached to stereo cameras to provide a 3D FPV view through a VR head-mounted display (Smolyanskiy and Gonzalez-Franco 2017). FPV drones can also be used in conjunction with mixed reality technology to achieve real-time interaction and coexistence of real and virtual objects (Kim et al. 2020). In the interactive system by the mixed reality environment mode, the user can control the drone by interacting with the system in order to achieve visual flight (Ai et al. 2016).
Matte painting is a technique widely used in filmmaking (Fan et al. 2014; Snider 2020). The design process needs to consider the light, color and texture of the environment. Mastering the relationship between camera motion and matte painting enhances the comprehension of filmmaking (Ramdhan and Nugraha 2020). In order to create images that are not obtrusive but also shocking to the audience, the fusion of virtual and real scenes is very important. Hence, this is a creative process that imitates the way the eye perceives an environment and scene (Hamus-Vallée 2015). The specific operation process requires the designer to replace a part of the real shooting scene with a virtual drawing image. Considering that the background of a video is relatively insignificant to the audience’s visual experience, some of the matte painting designers try to replace the original picture with a flat image (Yip 2020). A high-quality matte painting can be seamlessly integrated with the shot. The most common types of computer applications used in matte painting are 2D, 2.5D, and 3D (Eisenmann and Parent 2010). Using 2D technology, it is possible to generate backgrounds such as grand mountains or skies by arranging digital images on a flat surface (del Blanco García and García Ríos 2017). With 2.5D technology, multiple images are layered at multiple depths in a picture, creating an enhanced image. Matte painting using 3D technology has become a mainstream activity. Based on 3D modeling techniques, this method calculates the depth of a picture (Dallas 2011). When using this method for matte painting design and part of the original shooting scene is preserved, the rest is composed of virtual models.
The matte painting course focuses on improving the students’ perception of the environment and their ability to create virtual scenes that are realistic (Çakir et al. 2019). In the learning process, students’ observation and understanding of the real environment is especially important. In this context, using an FPV drone to observe the environment may help to address the focus and difficulty of the matte painting course. Meanwhile, the perception and perspectives of students as they use FPV drones are similar to the camera movement that needs to be considered in matte painting production. Therefore, this entry attempts to add an HCI factor to the matte painting course through the use of FPV drones to observe and experience the natural environment that needs to be painted.

3. Perceived Interactivity (PI)

Perceived interactivity is defined as the degree to which users are able to participate in changing the form and content of the HCI environment in real time (Kim et al. 2019). Users will generate corresponding evaluation of the man-machine interaction experience when they use different machines. Interactivity is the psychological experience a person has after experiencing a system (Newhagen et al. 1995). It is one of the primary determinants of user behavior (Reeves 1997). The student’s assessment of the interactive properties of the machines employed in the course is reflected in the interactivity of the learning process. Most users evaluate the perceived interactivity of the system through three dimensions, including control, responsiveness, and communication (Lu et al. 2019a). According to educational research using HCI functions, these dimensions reflect the opinions of students regarding the interaction process. The assessment consists of three components: the students’ perceptions of their abilities to control and understand the machine; whether the machine is capable of capturing their input and of providing timely and accurate responses; and whether the machine assists in promoting two-way communication in the course.

4. Perceived Vividness (PV)

Perceived vividness refers to how well the technology is used to create the sensory media environment (Bae et al. 2020). This rating represents the degree of similarity between the interactive or virtual environment created by the technology and the real environment. The perception of liveliness contributes to the enjoyment and immersion of the user (Deng et al. 2019). According to educational research, the perceived vividness of an interactive learning environment is closely related to the learning experience of the student. Vividness is one of the core elements of media richness (Lee 2022). The richer the design details of the media, the more vivid the learning environment for students. User searches and sharing behavior are positively influenced by the vividness of HCI (Zeng et al. 2022). Given the frequency of system iterations and usage cycles and habits in the course, the HCI learning style may therefore help students achieve a more active state of learning.

5. Novel Experience (NE)

The novel experience refers to the uniqueness and originality of the product in comparison with the user’s existing experience with other products after conducting a dedicated analysis on the product (Luan and Kim 2022). This is when users understand or use the new product and use the old product as a reference to evaluate the degree of change. Additionally, some studies have noted that novelty refers to the comprehensive evaluation results of interactive products as well as new, unique, and differentiated features (Yuan et al. 2021). Therefore, a novel experience in educational research refers to the comparisons between uniqueness and differentiation of a current educational method and the experience of the previous class. Researchers have found that users may feel strong emotions after experiencing a novel technology (Yim et al. 2017). This makes it possible to solve educational problems from a technical perspective. The ability to experience high levels of arousal and focus on current content is enhanced when new experiences are influencing stable cognition (Kim and Han 2014). Innovation in learning styles may influence students’ subsequent behavior and responses. It is for this reason that novelty in interactive education needs to be considered and evaluated.

6. The Flow Experience (FL)

The flow experience is a state in which a person is fully immersed in an ongoing process (da Silva deMatos et al. 2021). An individual in a flow state focuses on the difficulty and progress of the task. It was originally defined as the overall sensation people feel when they are fully engaged in action (Csikszentmihalyi 2014). The difficulty of the task is within the range of the user’s estimate of his ability. The status facilitates more focused learning and is therefore noted in the educational field (Wang et al. 2020). When the flow experience is triggered, people feel energized, engaged, and enjoy it (Huang et al. 2022). In order to achieve a flow experience in learning, teachers must pay attention to the behaviors and experiences of students during the interactive process. Flow is affected by three main factors, the perceived challenges and the user’s capabilities, the ability of the user to identify clear short-term goals, and feedback that is exact and immediate (Kushlev et al. 2020).

7. Trust (TR)

There is a strong correlation between students’ trust in teachers and motivation to learn (Mieziene et al. 2022). This suggests that trust seems to be an important prerequisite for helping students achieve a more positive state of learning. Researchers have attempted to test the relationship between trust and learning experiences in a number of studies. In previous studies, it has been shown that students’ trust in teachers is crucial for a positive learning environment (Zamora-Antuñano et al. 2021). Trust plays a significant role in ensuring an efficient learning process. A conversational learning environment requires trust between teachers and students, but trust can be undermined by misunderstandings, particularly during cross-cultural communication (Kandiko Howson et al. 2022). Thus, it is important to discuss effective methods of building trust in education and the effect of trust on the learning state.

8. Learning Interest (LI)

A person’s interest can be defined as the tendency to return to a particular category of objects, events, or ideas over a period of time (Tsai et al. 2018). An interest in the study will help the student to achieve a more willing-to-learn mentality. Interest is one of the key components of intellectual behavior (Murayama et al. 2019). Disinterested learning is passive and less efficient. The primary objective of education is to increase students’ interest in learning (Harackiewicz et al. 2012). The enhancement of students’ interest in learning contributes to more effective teaching. Research has shown that the adoption of innovative interactive technologies in educational settings affects students’ interest in learning (Chang et al. 2019). Therefore, when evaluating the use of FPV drones in curriculum design, students’ interest in learning in HCI environment should be carefully considered.

9. Continuous Learning Intention (CLI)

The learner’s continuous learning intention is based on their continuance intentions for the course. Guo et al. (2022) believed that a continuous learning intention was determined by a student’s willingness to participate continuously in the course. Continuous learning intention refers to a student’s judgment on whether to continue learning after course learning. This concept comes from Bhattacherjee, who proposed expectation confirmation theory (ECT), and who asserts that continued interest in information systems is similar to repeat purchases in consumption (Bhattacherjee 2001). The ECT model is a widely accepted method for predicting user-continuous behavior (Wang et al. 2021). In the field of pedagogy, this persistent behavior toward the system is often used to predict a learner’s continuous learning intention. Learners’ continuance intentions are a vital component of educational development (Li et al. 2021). In the research on promoting education digitization, continuous learning intention has received more attention because HCI experience has been added to the learning process. For example, previous studies have shown that, after completion of a blended online course, online interaction significantly impacts students’ willingness to further online learning (Zhu et al. 2020). Thus, the intention of students to engage in continuous learning is closely tied to the design and effect evaluation of their courses.

This entry is adapted from the peer-reviewed paper 10.3390/jintelligence10030040

References

  1. Tong, Yangfan, Weiran Cao, Qian Sun, and Dong Chen. 2021. The Use of Deep Learning and VR Technology in Film and Television Production From the Perspective of Audience Psychology. Frontiers in Psychology 12: 501.
  2. Bramley, Ian, Alastair Goode, Laura Anderson, and Elisabeth Mary. 2018. Researching in-store, at home: Using virtual reality within quantitative surveys. International Journal of Market Research 60: 344–51.
  3. Ramdhan, Zaini, and Novian Denny Nugraha. 2020. Set extention vfx analysis of visual effects from the wiro sableng film the movie. Paper presented at the Proceeding International Conference on Multimedia, Architecture, and Design, Chengdu, China, May 28–30.
  4. Wu, Wei-Long, Yen Hsu, Qi-Fan Yang, and Jiang-Jie Chen. 2021. A Spherical Video-Based Immersive Virtual Reality Learning System to Support Landscape Architecture Students’ Learning Performance during the COVID-19 Era. Land 10: 561.
  5. Hsu, Yen, Jiangjie Chen, Chao Gu, and Weilong Wu. 2021. Exploring the Flow Experience of Augmented Reality Applied to Basic Design Teaching. Journal of Design 26: 43–66.
  6. Gu, Chao, Jiangjie Chen, Jiacheng Lin, Shuyuan Lin, Weilong Wu, Qianling Jiang, Chun Yang, and Wei Wei. 2022a. The impact of eye-tracking games as a training case on students’ learning interest and continuous learning intention in game design courses: Taking Flappy Bird as an example. Learning and Motivation 78: 101808.
  7. Chen, Jiang-Jie, Yen Hsu, Wei Wei, and Chun Yang. 2021. Continuance intention of augmented reality textbooks in basic design course. Education Sciences 11: 208.
  8. Bolick, Madeleine M., Elena A. Mikhailova, and Christopher J. Post. 2022. Teaching Innovation in STEM Education Using an Unmanned Aerial Vehicle (UAV). Education Sciences 12: 224.
  9. Smolyanskiy, Nikolai, and Mar Gonzalez-Franco. 2017. Stereoscopic first person view system for drone navigation. Frontiers in Robotics and AI 4: 11.
  10. Kim, Dong-Hyun, Yong-Guk Go, and Soo-Mi Choi. 2020. An aerial mixed-reality environment for first-person-view drone flying. Applied Sciences 10: 5436.
  11. Ai, Zhuming, Mark A. Livingston, and Ira S. Moskowitz. 2016. Real-time unmanned aerial vehicle 3D environment exploration in a mixed reality environment. Paper presented at the 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA, USA, June 7–10.
  12. Fan, Shaojing, Tian-Tsong Ng, Jonathan S. Herberg, Bryan L. Koenig, Cheston Y.-C. Tan, and Rangding Wang. 2014. An automated estimator of image visual realism based on human cognition. Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, June 23–28.
  13. Snider, Marika. 2020. Using Cinematographic Tools for Historic House Digital Restorations. Paper presented at the 2020 Intermountain Engineering, Technology and Computing (IETC), Orem, UT, USA, October 2–3.
  14. Hamus-Vallée, Réjane. 2015. Matte painting or the quest for the perfect illusion. Analysis of the illusion processes of a hundred-year-old cinematographic trick. Hybrid. Revue des Arts et Médiations Humaines 2: 1–17.
  15. Yip, David Kei-Man. 2020. The Invisible Art of Storytelling and Media Production. Paper presented at the International Conference on Applied Human Factors and Ergonomics, San Diego, CA, USA, July 16–20.
  16. Eisenmann, Jonathan, and Rick Parent. 2010. Matte painting in stereoscopic synthetic imagery. Paper presented at the Stereoscopic Displays and Applications XXI, San Jose, CA, USA, January 18–20.
  17. del Blanco García, Federico Luis, and Ismael García Ríos. 2017. Technology transfer: From the film industry to architecture. In Architectural Draughtsmanship. Berlin and Heidelberg: Springer, pp. 105–18.
  18. Dallas, Sam. 2011. Blast from the past. Inside Film: If 143: 58–59.
  19. Çakir, Erdinç, Mahmut Sami Öztürk, and Ünal Mevlüt. 2019. Interpainting as a creating method in digital illustration: Reinterpretations from movie scenes. Bilim Eğitim Sanat ve Teknoloji Dergisi 3: 78–88.
  20. Kim, Sanghyun, Moon Jong Choi, and Jae Sung Choi. 2019. Empirical study on the factors affecting individuals’ switching intention to augmented/virtual reality content services based on push-pull-mooring theory. Information 11: 25.
  21. Newhagen, John E., John W. Cordes, and Mark R. Levy. 1995. : Audience scope and the perception of interactivity in viewer mail on the Internet. Journal of Communication 45: 164–75.
  22. Reeves, Thomas C. 1997. Effective dimensions of interactive learning on the World Wide Web. In Web-Based Instruction. Edited by Badrul H. Khan. Englewood Cliffs: Educational Technology Pubns.
  23. Lu, Dong, Ivan Ka Wai Lai, and Yide Liu. 2019a. The consumer acceptance of smart product-service systems in sharing economy: The effects of perceived interactivity and particularity. Sustainability 11: 928.
  24. Bae, Sujin, Timothy Hyungsoo Jung, Natasha Moorhouse, Minjeong Suh, and Ohbyung Kwon. 2020. The influence of mixed reality on satisfaction and brand loyalty in cultural heritage attractions: A brand equity perspective. Sustainability 12: 2956.
  25. Deng, Xiaoyan, Hanumantha Rao Unnava, and Hyojin Lee. 2019. “Too true to be good?” when virtual reality decreases interest in actual reality. Journal of Business Research 100: 561–70.
  26. Lee, Un-Kon. 2022. Tourism Using Virtual Reality: Media Richness and Information System Successes. Sustainability 14: 3975.
  27. Zeng, Yanfang, Lihua Liu, and Rui Xu. 2022. The effects of a virtual reality tourism experience on tourist’s cultural dissemination behavior. Tourism and Hospitality 3: 314–29.
  28. Luan, Yingyue, and Yeun Joon Kim. 2022. An integrative model of new product evaluation: A systematic investigation of perceived novelty and product evaluation in the movie industry. PLoS ONE 17: e0265193.
  29. Yuan, Chunlin, Shuman Wang, Xiaolei Yu, Kyung Hoon Kim, and Hakil Moon. 2021. The influence of flow experience in the augmented reality context on psychological ownership. International Journal of Advertising 40: 922–44.
  30. Yim, Mark Yi-Cheon, Shu-Chuan Chu, and Paul L. Sauer. 2017. Is augmented reality technology an effective tool for e-commerce? An interactivity and vividness perspective. Journal of Interactive Marketing 39: 89–103.
  31. Kim, Yoo Jung, and JinYoung Han. 2014. Why smartphone advertising attracts customers: A model of Web advertising, flow, and personalization. Computers in Human Behavior 33: 256–69.
  32. da Silva deMatos, Nelson Manuel, Elisabete Sampaio de Sa, and Paulo Alexandre de Oliveira Duarte. 2021. A review and extension of the flow experience concept. Insights and directions for Tourism research. Tourism Management Perspectives 38: 100802.
  33. Csikszentmihalyi, Mihaly. 2014. Toward a psychology of optimal experience. In Flow and the Foundations of Positive Psychology. Berlin and Heidelberg: Springer, pp. 209–26.
  34. Wang, Zhengpei, Xue Yang, and Xiaolu Zhang. 2020. Relationships among boredom proneness, sensation seeking and smartphone addiction among Chinese college students: Mediating roles of pastime, flow experience and self-regulation. Technology in Society 62: 101319.
  35. Huang, Shenghua, Hongbiao Yin, Yule Jin, and Wenlan Wang. 2022. More Knowledge, More Satisfaction with Online Teaching? Examining the Mediation of Teacher Efficacy and Moderation of Engagement during COVID-19. Sustainability 14: 4405.
  36. Kushlev, Kostadin, Danielle M. Drummond, Samantha J. Heintzelman, and Ed Diener. 2020. Do happy people care about society’s problems? The Journal of Positive Psychology 15: 467–77.
  37. Mieziene, Brigita, Arunas Emeljanovas, Roma Jusiene, Rima Breidokiene, Sigita Girdzijauskiene, Stanislav Sabaliauskas, Jolita Buzaityte-Kasalyniene, Virginija Budiene, Indre Eiliakaite, and Erika Speicyte-Ruschhoff. 2022. Direct and Indirect Effects of Social Support and School Social Capital on the Academic Success of 11–19-Year-Old Students Using Distance Learning. Sustainability 14: 2131.
  38. Zamora-Antuñano, Marco Antonio, Juvenal Rodríguez-Reséndiz, Miguel Angel Cruz-Pérez, Hugo Rodríguez Reséndíz, Wilfrido J. Paredes-García, and José Alfredo Gaytán Díaz. 2021. Teachers’ perception in selecting virtual learning platforms: A case of mexican higher education during the COVID-19 crisis. Sustainability 14: 195.
  39. Kandiko Howson, Camille, Ian M. Kinchin, and Karen Gravett. 2022. Belonging in Science: Democratic Pedagogies for Cross-Cultural PhD Supervision. Education Sciences 12: 121.
  40. Tsai, Ya-Hsun, Chien-Hung Lin, Jon-Chao Hong, and Kai-Hsin Tai. 2018. The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education 121: 18–29.
  41. Murayama, Kou, Lily FitzGibbon, and Michiko Sakaki. 2019. Process account of curiosity and interest: A reward-learning perspective. Educational Psychology Review 31: 875–95.
  42. Harackiewicz, Judith M., Christopher S. Rozek, Chris S. Hulleman, and Janet S. Hyde. 2012. Helping parents to motivate adolescents in mathematics and science: An experimental test of a utility-value intervention. Psychological Science 23: 899–906.
  43. Chang, Yuh-Shihng, Kuo-Jui Hu, Cheng-Wei Chiang, and Artur Lugmayr. 2019. Applying Mobile Augmented Reality (AR) to teach Interior Design students in layout plans: Evaluation of learning effectiveness based on the ARCS Model of learning motivation theory. Sensors 20: 105.
  44. Guo, Qian, Qingfeng Zeng, and Lanlan Zhang. 2022. What social factors influence learners’ continuous intention in online learning? A social presence perspective. Information Technology & People.
  45. Bhattacherjee, Anol. 2001. Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly 25: 351–70.
  46. Wang, Ting, Chien-Liang Lin, and Yu-Sheng Su. 2021. Continuance intention of university students and online learning during the COVID-19 pandemic: A modified expectation confirmation model perspective. Sustainability 13: 4586.
  47. Li, Yiwen, Norihiro Nishimura, Hisanori Yagami, and Hye-Sook Park. 2021. An empirical study on online learners’ continuance intentions in China. Sustainability 13: 889.
  48. Zhu, Yue, Jia Hua Zhang, Wing Au, and Greg Yates. 2020. University students’ online learning attitudes and continuous intention to undertake online courses: A self-regulated learning perspective. Educational Technology Research and Development 68: 1485–519.
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