Generative Art Creation
Examinig Generative Techniques in SideFX Houdini.
Master Thesis
Generative Art
Creation
This master thesis investigates generative tools and methods within SideFX Houdini, focusing on clarifying the definitions and processes behind generative art.
By examining different systems and workflows, the research explores generative systems for the creation of digital artworks inside Houdini, offering insights into the effectivness of the different generative tools and methods.
Generative Art Creation: Examining Generative Techniques in SideFX Houdini – A Digital Exhibition.
Methodology
This study conducted a qualitative comparative analysis (QCA) of eight generative artworks in Houdini, evaluating them based on visual quality and setup difficulty.
Each artwork used a base geometry for consistency, and generative methods were classified into Node, Network, and Code Implementations. Data from each method was collected, and a fuzzy set QCA (fsQCA) was performed using the fsQCA software to measure the effectiveness of the tools and methods.
This approach allowed for detailed comparison of generative systems across a spectrum of conditions, leading to insights on their effectiveness.
Artwork 01 – VDB Reaction Diffusion
Artwork 02 – Cyclic Cellular Automata
Artwork 03 – Voronoi Diagram
Artwork 04 – POP Differential Growth
Artwork 05 – Lindenmayer System
Artwork 06 – Particle System
Artwork 07 – Reaction Diffusion OpenCL
ARtwork 08 – Vellum Growth System
Results
The results of the study identified three generative methods in Houdini as being effective based on visual quality and setup difficulty.
Artwork 05 – L-System
Lindenmayer System
Node Implementation
Effectiveness: 0.95
Artwork 02 – Cyclic Cellular Automata
Cyclic Cellular Automata
Code Implementation
Effectiveness: 0.94
Artwork 04 – Differential Growth
Differential Growth System
Network Implementation
Effectiveness: 0.91
Through the process of identifying the generative methods in Houdini, three classifications of generative tools and methods in Houdini could be found:
Node Implementation
This class describes generative methods that are already implemented in Houdini through a node.
Network Implementation
Network Implementation describe systems that can be built with Houdini‘s nodes.
Code Implementation
The last class describes generative methods that are implemented mainly by the use of code.
A key finding was that Node Implementations are the most user-friendly, while Code Implementations require more effort but result in richer visual complexity. Further, the remaining 5 Artworks were measured as non-effective trough this study.
INFO
This page offers a brief overview of the results and outcomes of my Master’s thesis ‘Generative Art Creation: Examining Generative Techniques in SideFX Houdini.’ For more details or access to the full thesis, feel free to contact me.