MoodScape: Emotion-Informed Terrain Synthesis for Virtual Reality System

(1) Background: Virtual environments (VEs) significantly influence human emotions through various elements such as lighting, color, and terrain. While the effects of lighting and color on emotions within VEs have been extensively studied, the impact of the terrain remains underexplored. This paper addresses this gap by investigating the correlation between terrain characteristics in VEs and users’ emotional states. (2) Methods: We conducted a user study in which participants were exposed to various 3D terrains and used the Self-Assessment Manikin (SAM) to rate their emotional responses (valence, arousal, and dominance). Building on these insights, we propose MoodScape, an automated framework for emotion-informed terrain generation that significantly reduces the need for extensive expertise and manual effort. In the current implementation, continuous SAM valence–arousal targets are discretised into four quadrant-based affect/terrain classes, and this discrete class label conditions DH-CVAE-GAN terrain synthesis. MoodScape designs a generative adversarial network (GAN) architecture called DH-CVAE-GAN, which integrates a dual-head conditional variational autoencoder as the generator alongside a discriminator network to ensure effective and realistic terrain generation. The DH-CVAE-GAN is trained on a satellite-derived digital elevation model (DEM) dataset, which helps the generated terrains reflect realistic geographic patterns. (3) Results: Quantitative and qualitative evaluations on our study sample suggest that MoodScape can generate terrains whose perceived affective tone is broadly consistent with the specified affect-class inputs, indicating potential applications in gaming and exploratory therapeutic Virtual Reality, while formal clinical efficacy remains in future work.