生成工业装配场景异常图像，精准还原部件姿态关系。
PostureObjectstitch: Anomaly Image Generation Considering Assembly Relationships in Industrial Scenarios
- 分频解耦特征，渐进式生成细节并保持一致性
- 引入条件损失与几何先验，确保装配关系正确
- 在MureCom和自建DreamAssembly数据集上表现优异
图像生成技术可合成特定工况下的图像，以补充真实工业异常数据，提升异常检测模型性能。现有生成方法很少考虑工业组件在装配中的位姿与朝向，导致生成图像难以用于下游应用。为此，我们提出一种新型图像合成方法PostureObjectStitch，实现满足工业装配需求的精确生成。通过条件解耦方法将多视角输入图像分解为高频、纹理与RGB特征；特征时序调制机制在扩散模型的时间步中动态调整这些特征，实现从粗到细的渐进生成并保持一致性。为确保语义准确性，引入条件损失以增强关键工业元素，并设计几何先验引导组件定位，保证正确的装配关系。在新构建的DreamAssembly数据集及MureCom数据集上的全面实验，以及下游应用验证了该方法的卓越性能。
原文摘要 · Abstract (English)
Image generation technology can synthesize condition-specific images to supplement real-world industrial anomaly data and enhance anomaly detection model performance. Existing generation techniques rarely account for the pose and orientation of industrial components in assembly, making the generated images difficult to utilize for downstream application. To solve this, we propose a novel image synthesis approach, called PostureObjectStitch, that achieves accurate generation to meet the requirement of industrial assembly. A condition decoupling approach is introduced to separate input multi-view images into high-frequency, texture, and RGB features. The feature temporal modulation mechanism adapts these features across diffusion model time-steps, enabling progressive generation from coarse to fine details while maintaining consistency. To ensure semantic accuracy, we introduce a conditional loss that enhances critical industrial elements and a geometric prior that guides component positioning for correct assembly relationships. Comprehensive experimental results on the MureCom dataset, our newly contributed DreamAssembly dataset, and the downstream application validate the outstanding performance of our method.
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