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Learned Parametric Emotion Editing: Real-Time Affective Filtering for On-Device Social Media Video — arXiv:2609.21624

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21624
License (as archived): CC0-1.0
Archived: 2026-10-10T01:44:16.167Z
SHA-256 of the archived text: cf2bf78de2ccba7f6744a35aa7180ab370e8ddc596d16796eb7ad930eec817c0
Stable URL of this page: https://metraipolis.com/documento/dc_b975da930591

How to cite

Learned Parametric Emotion Editing: Real-Time Affective Filtering for On-Device Social Media Video — arXiv:2609.21624 — arXiv (en), https://arxiv.org/abs/2609.21624 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b975da930591 (Metraipolis archive dc_b975da930591, sha256 cf2bf78de2ccba7f6744a35aa7180ab370e8ddc596d16796eb7ad930eec817c0)

BibTeX

@misc{dcb975da930591,
  title = {Learned Parametric Emotion Editing: Real-Time Affective Filtering for On-Device Social Media Video — arXiv:2609.21624},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21624},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b975da930591; sha256 cf2bf78de2ccba7f6744a35aa7180ab370e8ddc596d16796eb7ad930eec817c0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21624. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-21 in cs.HC).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2609.21624 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T01:13:57.233Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).