Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers

Key points

• Emotions are expressed on a continuum and are naturally described in the valence-arousal (VA) space.1
• Existing VA-based image editors are typically training-based and domain-specific, limiting them to VA regions seen during training and making them brittle across datasets and domains.1
• Meanwhile, despite producing high-quality edits, state-of-the-art foundation image editors are not continuous affective controllers: non-linear emotion representations in text encoders makes prompt interpolation poorly calibrated, so accurate and continuous VA control remains unreliable.1
• Across three face datasets, we improve VA target accuracy by 25-29%, expand VA-plane coverage by 28-50%, and increase expression diversity by 29-49%, over the strongest baseline.1

Sources (1)

• [1]Barycentric Guidance: Turning Foundation Image Editors into Continuous Affective Controllers
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 02:47 PM

  To fill this gap, we introduce Barycentric Guidance, a training-free framework that turns pretrained foundation image editors into continuous affective controllers over VA, requiring no finetuning, extra data, or model changes.

  Emotions are expressed on a continuum and are naturally described in the valence-arousal (VA) space.

Extractive summary: sentences quoted from the sources.