Abstract
2D assembly diagrams are often abstract and hard to follow, creating a need for intelligent assistants that can monitor progress, detect errors, and provide step-by-step guidance. In mixed reality settings, such systems must recognize completed and ongoing steps from the camera feed and align them with the diagram instructions. Vision Language Models (VLMs) show promise for this task, but face a depiction gap because assembly diagrams and video frames share few visual features. To systematically assess this gap, we construct IKEA-Bench, a benchmark of 1,623 questions across 6 task types on 29 IKEA furniture products, and evaluate 19 VLMs (2B-38B) under three alignment strategies. Our key findings: (1) assembly instruction understanding is recoverable via text, but text simultaneously degrades diagram-to-video alignment; (2) architecture family predicts alignment accuracy more strongly than parameter count; (3) video understanding remains a hard bottleneck unaffected by strategy. A three-level mechanistic analysis further reveals that diagrams and video occupy disjoint ViT subspaces, and that adding text shifts models from visual to text-driven reasoning. These results identify visual encoding as the primary target for improving cross-depiction robustness. Project page: this https URL
Abstract (translated)
二维装配图通常抽象且难以理解，这催生了对能够监控进度、检测错误并提供逐步指导的智能助手的需求。在混合现实场景中，此类系统必须通过摄像头画面识别已完成和进行中的步骤，并将其与图示指令对齐。视觉语言模型（VLMs）在此任务中展现出潜力，但面临表征鸿沟——装配图与视频帧共享的视觉特征极少。为系统评估这一差距，我们构建了IKEA-Bench基准测试，涵盖29款宜家家具产品的6类任务共1623个问题，并评估了19个（20亿至380亿参数）视觉语言模型在三种对齐策略下的表现。核心发现如下：(1) 装配指令理解可通过文本恢复，但文本同时削弱了图-视频对齐能力；(2) 架构家族比参数量更显著影响对齐精度；(3) 视频理解仍是策略无法缓解的关键瓶颈。三级机理分析进一步揭示：图示与视频占据不同的ViT子空间，且添加文本会使模型从视觉推理转向文本驱动推理。这些结果指出，视觉编码是提升跨表征鲁棒性的主要优化方向。项目页面：this https URL
URL
https://arxiv.org/abs/2604.00913
