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

2026-09-18

AI Summary

研究通过学习模型实现实时情绪编辑，以减少社交视频对用户的情绪影响，采用MobileNetV4和FiLM技术，在保持视频质量的同时降低了用户的唤醒水平。

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.

Problem

• Problematic internet use
• emotional intensity
• visual content

Innovation

• Learned Parametric Emotion Editing
• Real-Time Affective Filtering
• MobileNetV4
• FiLM-based emotion conditioning