arXiv Computer Vision
Oct 26

Forging Emotions: a deep learning experiment on emotions and art

The paper "Forging Emotions" investigates how Generative Adversarial Networks (GANs) can be used to generate images that convey or trigger specific emotions. It highlights that current datasets for high‑level concepts like emotions are inadequate and suggests incorporating psychological emotional networks into dataset construction. The study also finds that simple color cues are insufficient for affect generation, advocating for deeper analysis of GAN structure and composition to create genuinely emotive imagery.

By Amalia Foka
arXiv Computer Vision
Sep 3

AffectDelta: Beyond Emotion Labels for Image Editing

AffectDelta is a new image editing framework that moves beyond single emotion labels by modeling edits as transitions between eight‑dimensional emotion distributions. It uses a frozen Emotion Distribution Predictor to estimate the source state and a signed difference vector to encode the desired change, which is then translated into context‑dependent semantic and appearance modifications via a transition encoder and a diffusion backbone. The authors introduce AffectPair‑249K, a dataset of 248,841 source‑target pairs covering both cross‑category and within‑category transitions, and show that AffectDelta outperforms six baselines in affective alignment and content preservation.

By Xingzu Zhan, Lin Gu, Ruogu Fang