BRIDGE：背景路由与孤立离散门控用于粗掩膜局部编辑
BRIDGE: Background Routing and Isolated Discrete Gating for Coarse-Mask Local Editing
AI总结：
BRIDGE通过背景路由和离散门控技术解决粗掩膜局部编辑中掩膜形状偏差问题，提升编辑区域与背景的稳定性与一致性。
摘要：
粗掩膜局部图像编辑要求模型修改用户指定区域同时保持周围场景不变。然而，粗糙掩膜常成为意外形状先验：掩膜会将生成对象拉向其偶然边界。本文研究这一失败现象，提出双区约束框架，使背景保持稳定而可编辑区域遵循指令而不继承掩膜轮廓。BRIDGE通过将掩膜置于DiT骨干外进行支持构建和融合，避免DiT内部掩膜注入和复制控制分支。它使用BridgePath生成，其中主路径保留背景上下文，而主体路径从独立噪声生成可编辑内容。受Qwen-Image实验启发，BRIDGE引入可学习的离散几何门控，用于token级位置嵌入路由。该门控允许主体token在融合区域借用背景锚定坐标或保持主体中心坐标以获得几何自由。在BRIDGE-Bench、MagicBrush和ICE-Bench上评估，BRIDGE在BRIDGE-Bench上将Local SigLIP2-T从0.262（FLUX.1-Fill）和0.390（ACE++）提升至0.503，同时在局部DINO和DreamSim上获得并行提升。在MagicBrush和ICE-Bench上的零样本结果进一步表明，其在 curated benchmark 之外仍具竞争力的对齐和源保持能力，同时添加的路由模块保持紧凑，参数仅为13.31M，相比ControlNet式复制分支。
英文摘要：
Coarse-mask local image editing asks a model to modify a user-indicated region while preserving the surrounding scene. In practice, however, rough masks often become unintended shape priors: instead of serving as flexible edit support, the mask can pull the generated object toward its accidental boundary. We study this failure as mask-shape bias and frame the task through a Two-Zone Constraint, where the background should remain stable while the editable region should follow the instruction without being forced to inherit the mask contour. BRIDGE addresses this setting by keeping masks outside the DiT backbone for support construction and blending, avoiding DiT-internal mask injection and copied control branches. It uses BridgePath generation, where a Main Path preserves background context and a Subject Path generates editable content from independent noise. Motivated by a diagnostic Qwen-Image experiment showing that positional embeddings and attention connectivity regulate which image context visual tokens reuse, BRIDGE introduces a learnable Discrete Geometric Gate for token-level positional-embedding routing. This gate lets subject tokens borrow background-anchored coordinates near fusion regions or keep subject-centric coordinates for geometric freedom. We evaluate BRIDGE on BRIDGE-Bench, MagicBrush, and ICE-Bench. On BRIDGE-Bench, BRIDGE improves Local SigLIP2-T from 0.262 with FLUX.1-Fill and 0.390 with ACE++ to 0.503, with parallel gains in local DINO and DreamSim. Zero-shot results on MagicBrush and ICE-Bench further indicate competitive alignment and source preservation beyond the curated benchmark, while the added routing module remains compact at 13.31M parameters compared with ControlNet-style copied branches.
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