🔍 Introduction

This is the official implementation for Object-IR (PR2026).

Tianli Liao¹, Ran Wang¹, Siqing Zhang¹, Lei Li¹, Guangen Liu¹, Chenyang Zhao¹, Heling Cao¹, Peng Li²

¹College of Information Science and Engineering, Henan University of Technology

²Institute for Complexity Science, Henan University of Technology

Pipeline

Given any aspect ratio, we construct a rigid mesh for the output resolution and estimate the grid's motion via a CNN-based regression network.

📝 Changelog

• 2025.12.05: The training code, dataset and pretrained model are online.
• 2025.11.03: The paper of the arXiv version is online.

🐍 Code

Requirement

• python 3.8.5
• numpy 1.24.4
• pytorch 2.4.1
• tensorboard 2.13.0

We implement Object-IR with one GPU of RTX3090. Refer to environment.yml for more details.

Training

Step 1: Download the dataset

First download the COCO dataset (or in Baidu Cloud, extraction code: 1205). Unzip and put the dataset in the "Data/" directory.

Step 2: Start training

Run the following command to start the training:

python train.py

The trained model will be saved in the "model/" directory.

Pre-trained model

The pre-trained model are available at Google Drive or Baidu Cloud (extraction code: 1205). Please download them and put them in the 'model' folder.

Testing

Step 1: Generate the warped images

Set the train/test dataset path in Codes/test_output.py and run:

python test_output.py

Step 2: Calculate the distortion error

Set the test dataset path in Codes/test.py and run

python test.py

📚 Citation

If you have any questions, please don't hesitate to contact me.

Tianli Liao -- tianli.liao@haut.edu.cn

@article{liao2026object-ir,
title = {Object-IR: Leveraging object consistency and mesh deformation for self-supervised image retargeting},
author = {Tianli Liao and Ran Wang and Siqing Zhang and Lei Li and Guangen Liu and Chenyang Zhao and Heling Cao and Peng Li},
journal = {Pattern Recognition},
volume = {172},
pages = {112651},
year = {2026},
}