MultiBind: A Benchmark for Attribute Misbinding in Multi-Subject Generation

TL;DR

MultiBind is a new benchmark designed to evaluate and diagnose attribute misbinding issues in multi-subject image generation, revealing failure modes undetected by traditional metrics.

Contribution

It introduces a comprehensive benchmark with a novel evaluation protocol that isolates cross-subject interference in multi-entity image generation tasks.

Findings

Reveals binding failures missed by conventional metrics

Identifies interpretable failure patterns such as drift and swap

Provides a dimension-wise confusion evaluation protocol

Abstract

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.