# crea.blender: A Neural Network-Based Image Generation Game to Assess Creativity

arXiv: https://arxiv.org/abs/2008.05914 · HTML: https://mlsanity.com/paper/2008.05914/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information](https://mlsanity.com/paper/2202.00965/) (2022)
- [Circuit Complexity Bounds for Visual Autoregressive Model](https://mlsanity.com/paper/2501.04299/) (2025)
- [The Animal-AI Environment: A Virtual Laboratory For Comparative Cognition and Artificial Intelligence Research](https://mlsanity.com/paper/2312.11414/) (2023)

## Abstract (arXiv)

We present a pilot study on crea.blender, a novel co-creative game designed for large-scale, systematic assessment of distinct constructs of human creativity. Co-creative systems are systems in which humans and computers (often with Machine Learning) collaborate on a creative task. This human-computer collaboration raises questions about the relevance and level of human creativity and involvement in the process. We expand on, and explore aspects of these questions in this pilot study. We observe participants play through three different play modes in crea.blender, each aligned with established creativity assessment methods. In these modes, players "blend" existing images into new images under varying constraints. Our study indicates that crea.blender provides a playful experience, affords players a sense of control over the interface, and elicits different types of player behavior, supporting further study of the tool for use in a scalable, playful, creativity assessment.