Venkatesh Thirugnana Sambandham, Torsten Schon · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29651 · open-access PDF
在多人场景生成中,这就造成了妥协:全球控制只通过在图像中对人进行同质化来改善总体平衡。基于控制的方法,如嵌入算法,将内部图像的多样性推向不受控制的基线以下。文本和隐形投射方法改变了总体偏差,在图像内部多样性中只有边际收益。
Machine translation (Meta M2M100); the English original follows.
Text-to-Image models reproduce societal stereotypes, and existing mitigation applies one demographic correction to the whole scene. In multi-person scene generation this creates a trade-off: global steering improves aggregate balance only by homogenizing the people within an image. Steering-based methods like Embedding Arithmetic drive the intra-image diversity below the unsteered baseline. Text- and latent-projection approaches shift the aggregate skew with only marginal gains in intra-image diversity. We present Spatially Gated Steering (SGS), which steers the demographics of each person ind
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