EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model
Journal:
arXiv
Published Date:
Jan 10, 2025
Abstract
Recent research shows that emotions can enhance users' cognition and
influence information communication. While research on visual emotion analysis
is extensive, limited work has been done on helping users generate emotionally
rich image content. Existing work on emotional image generation relies on
discrete emotion categories, making it challenging to capture complex and
subtle emotional nuances accurately. Additionally, these methods struggle to
control the specific content of generated images based on text prompts. In this
work, we introduce the new task of continuous emotional image content
generation (C-EICG) and present EmotiCrafter, an emotional image generation
model that generates images based on text prompts and Valence-Arousal values.
Specifically, we propose a novel emotion-embedding mapping network that embeds
Valence-Arousal values into textual features, enabling the capture of specific
emotions in alignment with intended input prompts. Additionally, we introduce a
loss function to enhance emotion expression. The experimental results show that
our method effectively generates images representing specific emotions with the
desired content and outperforms existing techniques.
