Affect-Conditioned Image Generation

• URL: http://arxiv.org/abs/2302.09742v1
• Date: Mon, 20 Feb 2023 03:44:04 GMT
• Title: Affect-Conditioned Image Generation
• Authors: Francisco Ibarrola, Rohan Lulham and Kazjon Grace
• Abstract summary: We introduce a method for generating images conditioned on desired affect, quantified using a psychometrically validated three-component approach. We first train a neural network for estimating the affect content of text and images from semantic embeddings, and then demonstrate how this can be used to exert control over a variety of generative models.
• Score: 0.9668407688201357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In creativity support and computational co-creativity contexts, the task of discovering appropriate prompts for use with text-to-image generative models remains difficult. In many cases the creator wishes to evoke a certain impression with the image, but the task of conferring that succinctly in a text prompt poses a challenge: affective language is nuanced, complex, and model-specific. In this work we introduce a method for generating images conditioned on desired affect, quantified using a psychometrically validated three-component approach, that can be combined with conditioning on text descriptions. We first train a neural network for estimating the affect content of text and images from semantic embeddings, and then demonstrate how this can be used to exert control over a variety of generative models. We show examples of how affect modifies the outputs, provide quantitative and qualitative analysis of its capabilities, and discuss possible extensions and use cases.

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