This is SBXZY, trying to build a complete virtual world
with visuals, cultures and actions.
The creation process is planned to be implemented mostly by artificial intelligence.
- Parameterized model generation (partially):
There are Generative adversarial networks (GAN) methods
that generate 3D models. But the neural network
generator is limited by both effectiveness and
efficiency. Instead, we use two primitives, graphs and
pairs of edges, to generate all 3D models, so that
artificial intelligence generating of models are easier.
- Procedual animations: Animations are just
affine transformations if expressed in the Biovision hierarchical
data (bvh) format. These
transformations can be modeled by equations and
parameters. This is implemented together with the
parameterized model generator.
- Interaction mechanism: Interaction mechanisms
including physics simulations and visualization of
virtual worlds are implemented by the Panda3D
- Music composer: Music composing is carried out
in a 'semi automatic' manner. In GAN methods, users have
little control of the generating process. In SBXZY's
software, AI aids creating music sequences instead of
doing it all, so that there is a higher chance AI can
compose the type of music the user wants.
- Planet generator: This is done by many people many times, but we implemented with fractals and Perlin noises combined.
||Evolve new cultures in new environments.|
The parameterized 3D model generator. Parameterized but not yet automated.
Generate bvh animations from patameters. Generated animations can be used in Panda3D and Blender.
Planet terrains generator and visual explorer. Created an area of 5000km times 5000km with just 300MB of data.
A visual chatbot, demonstrating lip syncing for Panda3D, with expressions generated by GAN.
 Xiang, Z. , Guo, Y. Controlling Melody Structures in
Automatic Game Soundtrack Compositions With Adversarial
Learning Guided Gaussian Mixture Models. IEEE transactions
on Games (2021), 13,2 193-204. DOI: 10.1109/TG.2020.3035593
 Xiang, Z., Zhou, K., Guo, Y., Gaussian mixture noised random fractals with adversarial learning for automated creation of visual objects, Fractals - Complex geometry, patterns, and scaling in nature and society, (2020). 28,4. DOI: 10.1142/S0218348X20500681
 Xiang, Z. (2019). Towards automatic robot design: a black box function optimization approach. ChinaAvix. DOI: 10.12074/201905.00052
 Guo, Y., Fan, Y., Xiang, Z., Wang H., Meng, W., Xu, M. Zero-sample surface defect detection and classification based on semantic feedback neural network (2021) arXiv:2106.07959v1
 Xiang, Z. , Xiang C, Li T, Guo, Y. A self-adapting hierarchical actions and structures joint optimization framework for automatic design of robotic and animation skeletons. Softcomputing (2021), 25,3 DOI: 10.1007/s00500-020-05139-5.
 Xiang, Z., Xiao, Z., Wang, D., Xiao, J. (2017). Gaussian kernel smooth regression with topology learning neural networks and Python implementation. Neurocomputing, 260, 1-4. doi: http://dx.doi.org/10.1016/j.neucom.2017.01.051