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arXiv · 2609.21624

Learned Parametric Emotion Editing: Real-Time Affective Filtering for On-Device Social Media Video

Musa Rochi·Marcel Schubert·Christoph Gebhardt
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Abstract

Problematic internet use affects a growing share of the population, yet common interventions, e.g., time limits, blocking, forced breaks, are coercive and easily circumvented. We explore a less restrictive alternative: adapting the emotional intensity of visual content. Prior work has shown that optimization can steer an image's affective content, but its per-image optimization cost makes it impractical for real-time deployment. We instead learn a model that predicts this transformation in a single forward pass: a MobileNetV4 backbone with FiLM-based emotion conditioning outputs parameters for differentiable global transformations. This replaces prior iterative optimization (80 s per image) with a single 3.7 ms forward pass. In a user study (N = 54), the model reduced viewer-reported arousal relative to unedited images, comparably to the grayscale well-being filter, while being rated higher in perceived quality. We integrate the model into an Android app that adapts Instagram video in real time, sustaining 60 fps on a Samsung Galaxy S23.

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Musa Rochi, Marcel Schubert, Christoph Gebhardt. 2026-09-18. Learned Parametric Emotion Editing: Real-Time Affective Filtering for On-Device Social Media Video. https://arxiv.org/abs/2609.21624

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