EMOTION MODELING IN SCULPTURE DESIGN USING NEURAL NETWORKS

The paper introduces a unified method of designing sculptures on a feeling-sensitive neural network basis. The proposed Emotion-Form Neural Embedding Network (EFNEN) is based on the combination of Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to learn emotion-related correlations between sculptural form and emotion. The system was trained and tested using a selection of 1,200 annotated 3D models that had both geometric and a set of 3 emotion labels (valence and arousal) assigned to them. EFNEN obtained a correlation coefficient (r = 0.88) and 92.4% accuracy with human perceptual ratings, which was better than the baseline models. Latent emotion space and feature-emotion heatmap visualizations showed that the predictors of positive affect are curvature, symmetry, and balance. The model facilitates the classification of emotions as well as emotion-driven three dimensional form generation, thus leading to collaborative co-creation of artists and AI systems. The findings indicate that emotion is calculally formulated and synthesized to form a measurable aesthetic dimension, which makes EFNEN a platform of affective computational art and human-AI creative synergy.