Summary of An End-to-end Depth-based Pipeline For Selfie Image Rectification, by Ahmed Alhawwary et al.
An End-to-End Depth-Based Pipeline for Selfie Image Rectification
by Ahmed Alhawwary, Phong Nguyen-Ha, Janne Mustaniemi, Janne Heikkilä
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an end-to-end deep learning-based rectification pipeline to mitigate perspective distortion in close-up portraits. The proposed pipeline learns to predict facial depth using a deep CNN, adjusts the camera-to-subject distance, and reprojects 3D image features to the new perspective. An inpainting module is used to fill in missing pixels, and an auxiliary module predicts camera movement to reduce hallucination. Unlike previous works, this method processes full-frame input images without cropping, eliminating post-processing steps. The pipeline is trained using a large synthetic face dataset generated by Unreal Engine. Results show that the proposed pipeline outperforms previous methods and produces comparable results with 3D GAN-based methods while being faster than 260 times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in taking close-up pictures of faces. When we take a selfie or picture from up close, it can look distorted because of the way our eyes are positioned on our face. The authors create an artificial intelligence system that can correct this distortion by adjusting the camera’s angle and filling in missing parts. They train their system using a large number of fake pictures they created to simulate different faces, head positions, and lighting conditions. The results show that their system works better than previous methods and is much faster. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Gan » Hallucination
