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Cinematography affects how the audience perceives a movie. A same story plot can be interpreted differently through the presentation of different camera movements, which show the importance of cinematography in filmmaking. Typically, filmmaking is costly, and beginners and amateurs rarely have the opportunity to play and do an experiment on a film set. In this work, we aim to design and construct a virtual environment for film shooting, allowing a user to play multiple roles in a virtual film set and emulating the process of the filmmaking. Our system provides camera shooting assistants, tools for field directing and real-time editing, aiming to help novices learn cinematographic concepts, track the progress of filmmaking, and create a personalized movie. In order to verify that our system is a user-friendly and effective tool for experiencing filmmaking, we have conducted an experiment to observe the behaviors and obtain feedback from participants with various cinematographic backgrounds.

Creatives in animated and real movie productions have been exploring new modalities to visually design filmic sequences before realizing them in studios, through techniques like hand-drawn storyboards, physical mockups or more recently virtual 3D environments. A central issue in using virtual 3D environments is the complexity of content creation tools for non technical film creatives. To overcome this issue, we present One Man Movie, a VR authoring system which enables the crafting of filmic sequences with no prior knowledge in 3D animation. The system is designed to reflect the traditional creative process in film pre-production through stages like (i) scene layout (ii) animation of characters, (iii) placement and control of cameras and (iv) montage of the filmic sequence, while enabling a fully novel and seamless back-and-forth between all stages of the process thanks to real-time engines. This research tool has been designed and evaluated with students and experts from film schools, and should therefore raise a significant interest among Siggraph participants.

In the recent years, due to the prevalence of 3D printers, there have been many applications of 3D printing developed for education such as creating custom-made teaching aids or student art work. Although there are web sites hosting 3D models created by students and allowing them to share and search in the database, these systems usually lack functions of collaborative learning. On the other hand, most learning management systems do not provide functions needed for sharing and viewing 3D models. In this work, we aim to design a system called 3D Model Co-learning Space (3D MCLS), dedicated to collaborative learning allowing students to share 3D models as well as performing blind peer assessment. The system allows a user to store, share, display, and discuss 3D models and allows a teacher to manage a group of students in a flexible way. More specifically, the system can automatically create a thumbnail by computing an optimized view of an uploaded 3D model. It uses tags to organize models into groups according to their attributes or teams in a class for easier access through search. In addition, it can provide blind assignments of peer review and compare the results with self-assessment. We have implemented such a system and conducted a pilot study to obtain a preliminary evaluation on the usability of the system.

Target location is an important task in robotics applications. For different application purposes, the positions of targets are usually described by various coordinate systems. Closed-form formulas that describe the relationships between two coordinate systems serve as a means for coordinate transformation. However, the existence of unavoidable measurement errors and uncertainty makes closed-form formulas less reliable. Besides, the closed-form formulas usually involve operations of matrix inversion and transpose that usually consume a considerable amount of computing resources. This paper defines the problem of coordinate transformation on mobile robots as a regression problem and employs the techniques of gene expression programming to discover the regression models. With such regression models, coordinate transformation can be done by simpler formulas with lower processing costs. The proposed techniques have been implemented and integrated with a four-wheeled robot equipped with vision sensors and have been verified in real environments. The experiments demonstrate the effectiveness and performance of the proposed method. To the best of our knowledge, this is the first study on the underlying problem using genetic-based techniques.