Table showing object definitions, elements of the city, for inclusion. Continue reading objects…

Today we made some significant progress on the development of both the architecture and the implementation of the client/server infrastructure.

Architecture
On the client side, we will have a small “client manager”, written in Java, which will communicate with both the VRML player and the server, coordinating the two.

Implementation
A super basic version of the Client Manager is now working. All it does is connect to an InstantPlayer instance running on the same machine, and instantiate protos.

The next steps of the implementation process will be:

1. a basic version of the Server with a simple database
2. an improved Client Manager that can determine how the user has moved, and communicate this motion to the server
3. logic on the Server to share this motion information with all other Client Managers
4. logic on the Client Manager to receive motion/position information for other users and update InstantPlayer instance accordingly

When people enter the site, they enter adjacent to the last person who was there. Near the entry point City is most congested, full of structures left behind, those that did not participate. Structures that are visited often are full of ‘energy’, their colors and textures are dependent on people visiting them. There are also structures beyond the visible, the farthest out that people have ventured, the farthest out that people have congregated. We are wondering how to create a series of breadcrumbs, dots, lines or signposts that show the way to these hidden places.

Each avatar has a proximity sensor. When a person crosses this sensor, the sensor keeps track of how long a person has been there. When they leave the avatar or simply exit, the time they were there is sent to the server. When the server loads the main file again, it sends a list of criteria for each element in the file.

Currently we are testing InstantReality as a possible engine for the project.
An x3d player, it reads vrml!

Test Avatar

For this avatar I did the following:
- Took a headline from Google News… about AIG.
- Put every word into a Flickr search, selected first image from each search, with creative commons.
- Attached each texture to a part of a random model.

We will create a workshop to evaluate what criteria to use for avatar creation.