Summary of Selfswapper: Self-supervised Face Swapping Via Shape Agnostic Masked Autoencoder, by Jaeseong Lee et al.
SelfSwapper: Self-Supervised Face Swapping via Shape Agnostic Masked AutoEncoder
by Jaeseong Lee, Junha Hyung, Sohyun Jeong, Jaegul Choo
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers introduce the Shape Agnostic Masked AutoEncoder (SAMAE), a novel self-supervised approach for face swapping that combines the strengths of target-oriented and source-oriented methods. The SAMAE training scheme addresses limitations in traditional approaches by circumventing the seesaw game and introducing clear ground truth through self-reconstruction training. This leads to more stable model training, accurate reflection of target image skin color and illumination, and mitigation of identity leakage. The paper also tackles shape misalignment and volume discrepancy problems using new techniques like perforation confusion and random mesh scaling. As a result, SAMAE establishes a new state-of-the-art in face swapping, preserving both identity and non-identity attributes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Face swapping is a technology that can change people’s faces in videos or photos. Most previous approaches to face swapping have used a training method called the target-oriented approach. However, this often leads to unstable training and produces images with blended identities. Other methods try to fix these issues but still struggle to accurately reflect the skin color and lighting of the original image. This paper introduces a new approach called SAMAE that combines the strengths of both types of methods. The SAMAE method addresses the limitations of traditional approaches by introducing clear ground truth during training, which leads to more stable training and accurate results. The paper also talks about some challenges they faced in developing SAMAE and how they overcame them. |
Keywords
» Artificial intelligence » Autoencoder » Self supervised
