Creatives in animation and film productions have forever been exploring the use of new means to prototype their visual sequences before realizing them, by relying on hand-drawn storyboards, physical mockups or more recently 3D modelling and animation tools. However these 3D tools are designed in mind for dedicated animators rather than creatives such as film directors or directors of photography and remain complex to control and master. In this paper we propose a VR authoring system which provides intuitive ways of crafting visual sequences, both for expert animators and expert creatives in the animation and film industry. The proposed system is designed to reflect the traditional process through (i) a storyboarding mode that enables rapid creation of annotated still images, (ii) a previsualisation mode that enables the animation of the characters, objects and cameras, and (iii) a technical mode that enables the placement and animation of complex camera rigs (such as cameras cranes) and light rigs. Our methodology strongly relies on the benefits of VR manipulations to re-think how content creation can be performed in this specific context, typically how to animate contents in space and time. As a result, the proposed system is complimentary to existing tools, and provides a seamless back-and-forth process between all stages of previsualisation. We evaluated the tool with professional users to gather experts' perspectives on the specific benefits of VR in 3D content creation.

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.

The IR-drop problem during test mode exacerbates delay defects and results in false failures. In this paper, we take theX-fillingapproach to reduce the IR-drop effect during an at-speed test. The main difference between our approach and the previous X-filling approaches lies in two aspects. The first one is that we take the spatial information into consideration in our approach. The second one is how X-filling is performed. We propose a backward-propagation technique instead of a forward-propagation approach taken in previous work. The experimental results show that our approach can reduce 21.1% of the maximum IR-drop in the best case and 9.1% on the average as compared to previous work.

IR-drop problem during test mode exacerbates delay defects and results in false failures. In this paper, we take the X-filling approach to reduce IR-drop effect during at-speed test. The main difference between our approach and the previous X-filling methods [7]–[9] lies in two aspects. The first one is that we take the spatial information into consideration in our approach. The second one is how X-filling is performed. We propose a backward-propagation approach instead of a forward-propagation approach taken in previous work. The experimental results show that we have 42.81% reduction for the worst IR-drop and 45.71% reduction in the average IR-drop as compared to random fill method.