MultiBind: A Benchmark for Attribute Misbinding in Multi-Subject Generation
Subject-driven image generation is increasingly expected to support fine-grained control over multiple entities within a single image. In multi-reference workflows, users may provide several subject images, a background reference, and long, entity-indexed prompts to control multiple people within one scene. In this setting, a key failure mode is cross-subject attribute misbinding: attributes are preserved, edited, or transferred to the wrong subject. Existing benchmarks and metrics largely emphasize holistic fidelity or per-subject self-similarity, making such failures hard to diagnose. We introduce MultiBind, a benchmark built from real multi-person photographs. Each instance provides slot-ordered subject crops with masks and bounding boxes, canonicalized subject references, an inpainted background reference, and a dense entity-indexed prompt derived from structured annotations. We also propose a dimension-wise confusion evaluation protocol that matches generated subjects to ground-truth slots and measures slot-to-slot similarity using specialists for face identity, appearance, pose, and expression. By subtracting the corresponding ground-truth similarity matrices, our method separates self-degradation from true cross-subject interference and exposes interpretable failure patterns such as drift, swap, dominance, and blending. Experiments on modern multi-reference generators show that MultiBind reveals binding failures that conventional reconstruction metrics miss.
69c3468795b48f16a300778c
Wenqing Tian
69c3468795b48f16a300778d
Hanyi Mao
69c3468795b48f16a300778e
Zhaocheng Liu
claimed_verified
2026-03-25T10:50:37.914Z
633e570be7d5ce7bfe037a53
https://cdn-avatars.huggingface.co/v1/production/uploads/633e570be7d5ce7bfe037a53/zV8ULv4Mu7YIGZ8D3JtmK.jpeg
Zhaocheng Liu
zhaocheng
user
zhaocheng
69c3468795b48f16a300778f
Lihua Zhang
69c3468795b48f16a3007790
Qiang Liu
69c3468795b48f16a3007791
Jian Wu
69c3468795b48f16a3007792
Liang Wang
MultiBind: A Benchmark for Attribute Misbinding in Multi-Subject Generation
Subject-driven image generation is increasingly expected to support fine-grained control over multiple entities within a single image. In multi-reference workflows, users may provide several subject images, a background reference, and long, entity-indexed prompts to control multiple people within one scene. In this setting, a key failure mode is cross-subject attribute misbinding: attributes are preserved, edited, or transferred to the wrong subject. Existing benchmarks and metrics largely emphasize holistic fidelity or per-subject self-similarity, making such failures hard to diagnose. We introduce MultiBind, a benchmark built from real multi-person photographs. Each instance provides slot-ordered subject crops with masks and bounding boxes, canonicalized subject references, an inpainted background reference, and a dense entity-indexed prompt derived from structured annotations. We also propose a dimension-wise confusion evaluation protocol that matches generated subjects to ground-truth slots and measures slot-to-slot similarity using specialists for face identity, appearance, pose, and expression. By subtracting the corresponding ground-truth similarity matrices, our method separates self-degradation from true cross-subject interference and exposes interpretable failure patterns such as drift, swap, dominance, and blending. Experiments on modern multi-reference generators show that MultiBind reveals binding failures that conventional reconstruction metrics miss.
A new benchmark and evaluation method for multi-subject image generation that identifies and analyzes cross-subject attribute misbinding failures not detected by traditional metrics.
multi-reference workflows subject images entity-indexed prompts cross-subject attribute misbinding MultiBind benchmark slot-ordered subject crops canonicalized subject references inpainted background reference dense entity-indexed prompt dimension-wise confusion evaluation protocol slot-to-slot similarity face identity appearance pose expression self-degradation cross-subject interference interpretable failure patterns drift swap dominance blending