# PostureObjectstitch: Anomaly Image Generation Considering Assembly Relationships in Industrial Scenarios

Source: [PostureObjectstitch: Anomaly Image Generation Considering Assembly Relationships in Industrial Scenarios](<https://www.sota2.com/research/paper/69e06d057d0172140ec7ca24>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-17T05:00:13.532Z

Authors: Zebei Tong, Hongchang Chen, Yujie Lei, Gang Chen, Yushi Liu, Zhi Zheng, Hao Chen, Jieming Zhang, Ying Li, Dongpu Cao

Published: 2026-04-15T13:29:08.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on DreamAssembly](<https://www.sota2.com/research/sota/image-classification-on-dreamassembly.md>): Image Classification; DreamAssembly; Accuracy: 80.

- [Image Synthesis on MureCom](<https://www.sota2.com/research/sota/image-synthesis-on-murecom.md>): Image Synthesis; MureCom; CLIP-I: 0.8143.

- [Image Synthesis on DreamAssembly (test)](<https://www.sota2.com/research/sota/image-synthesis-on-dreamassembly-test.md>): Image Synthesis; DreamAssembly (test); CLIP-I: 0.8563.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.13863>)

[PDF](<https://arxiv.org/pdf/2604.13863.pdf>)