PostureObjectstitch: Anomaly Image Generation Considering Assembly Relationships in Industrial Scenarios

TL;DR

PostureObjectStitch is a novel image synthesis method designed for industrial scenarios, accurately generating assembly-related images by considering component pose, orientation, and relationships to improve anomaly detection.

Contribution

The paper introduces a new image generation approach that incorporates assembly relationships and pose information, with a condition decoupling method and geometric priors for industrial applications.

Findings

Achieves accurate industrial assembly image synthesis.

Enhances downstream anomaly detection performance.

Validated on MureCom and DreamAssembly datasets.

Abstract

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.