DepthArb: Training-Free Depth-Arbitrated Generation for Occlusion-Robust Image Synthesis

2026-03-25

AI Summary

This work addresses the frequent logical errors in occlusion relationships—particularly within densely overlapping regions—that plague current text-to-image diffusion models. To resolve this, the authors propose a training-free, plug-and-play framework that introduces, for the first time, a depth-aware Attention Arbitration Mechanism (AAM) to modulate inter-object attention competition in accordance with realistic depth ordering. This mechanism is complemented by Spatial Compactness Control (SCC) to enhance structural coherence. Evaluated on the newly introduced OcclBench benchmark, the method demonstrates superior performance over state-of-the-art approaches in both occlusion accuracy and visual fidelity, significantly strengthening the spatial hierarchy representation capabilities of diffusion models.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Text-to-image diffusion models frequently exhibit deficiencies in synthesizing accurate occlusion relationships of multiple objects, particularly within dense overlapping regions. Existing training-free layout-guided methods predominantly rely on rigid spatial priors that remain agnostic to depth order, often resulting in concept mixing or illogical occlusion. To address these limitations, we propose DepthArb, a training-free framework that resolves occlusion ambiguities by arbitrating attention competition between interacting objects. Specifically, DepthArb employs two core mechanisms: Attention Arbitration Modulation (AAM), which enforces depth-ordered visibility by suppressing background activations in overlapping regions, and Spatial Compactness Control (SCC), which preserves structural integrity by curbing attention divergence. These mechanisms enable robust occlusion generation without model retraining. To systematically evaluate this capability, we propose OcclBench, a comprehensive benchmark designed to evaluate diverse occlusion scenarios. Extensive evaluations demonstrate that DepthArb consistently outperforms state-of-the-art baselines in both occlusion accuracy and visual fidelity. As a plug-and-play method, DepthArb seamlessly enhances the compositional capabilities of diffusion backbones, offering a novel perspective on spatial layering within generative models.

Problem

• occlusion
• depth order
• image synthesis
• diffusion models
• spatial layout

Innovation

• Depth-Arbitrated Generation
• Attention Arbitration Modulation
• Spatial Compactness Control
• Occlusion-Robust Synthesis
• Training-Free Diffusion