The Visual Computer

When we design the computer games, new concepts are hard to communicate because the development of the new computer games is far less formalized compared with other software. However, the creative processing additionally has the benefit if the new concepts of the computer games are widely used and discussed. We focus on understanding the ideas to be shared between the game designer and the game programmer in the game design. The research defines a graph-based representation of game scenarios is in terms of Event graph, State graph, and Action graph forms in order to minimize anomalies of game flow design. This method in our research can reduce unexpected and undesirable game situations. Also it reduces the mismatched information between the game designer and the game programmer in game-playing. Compared with the existing method such as storyboard, flowchart, and SSM, we demonstrate the viability of our game design methodology.

The multi-view of an object can be used for 3D reconstruction. The method proposed in this paper generates the left and the top view of a target car through deep learning. The input of the method is only a front view of a 3D car and it isn’t necessary for the depth of the 3D car. Firstly, a rough orthographic views of the 3D car is gotten from an information constraint network which is constructed by considering the structural relation between one view and the other two views. And then the rough orthographic views is transformed into large-pixel block rough view through the nearest interpolation, at the same time, the large-pixel blocks are also migrated to improve the quality of the rough orthographic views. Finally, the generative adversarial network with perception loss is used to enhance the large-pixel block view. In addition, the three views generated by the network can be used to synthesize a 3D point cloud shell.

Light leaking in Probe GI is typically solved by visibility tests, which cannot benefit from hardware-aided tri-linear sampling. We present Mask Decomposition, which decomposes the visibility into probe-group indicators and their corresponding masks, making it possible to use tri-linear sampling in its reconstruction. We prove that the rendering overhead is significantly reduced with the help of Mask Decomposition, making the rendering at least $$3\times $$ faster than the state-of-the-art visibility-test Probe GI, and even as fast as the original leaking probe GI. We also present an efficient algorithm to solve the Mask Decomposition problem and a simple compression method to minimize the spatial overhead of the mask textures, which is much lower than in compression methods like Moving Basis Decomposition (MBD).

This paper presents a method for detecting points of interest on 3D meshes. It comprises two major stages. In the first, we capture saliency in the spectral domain by detecting spectral irregularities of a mesh. Such saliency corresponds to the interesting portions of surface in the spatial domain. In the second stage, to transfer saliency information from the spectral domain to the spatial domain, we rely on spectral irregularity diffusion (SID) based on heat diffusion. SID captures not only the information about neighbourhoods of a given point in a multiscale manner, but also cues related to the global structure of a shape. It thus preserves information about both local and global saliency. We finally extract points of interest by looking for global and local maxima of the saliency map. We demonstrate the advantages of our proposed method using both visual and quantitative comparisons based on a publicly available benchmark.

The complexity of abstract art languages create new semantics in art. This model tries to implement its conceptual language within creating a new virtual environment. The gap between art and technology has been approached with a fuzzy logic engine which uses a red and black semantic codification. The examples include the application of this metalanguage to a virtual artwork experimentation.

In this paper, we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances: the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent’s local space. This egocentric property allows us to efficiently compute a local space-time plan and has better parallel scalability than a global fields approach. We then use these perception fields to compute a fitness measure for every possible action, defined as an affordance field. The action that has the optimal value in the affordance field is the agent’s steering decision. We propose an extension to a linear space-time prediction model for dynamic collision avoidance and present our parallelization results on multicore systems. We analyze and evaluate our framework using a comprehensive suite of test cases provided in SteerBench and demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations.