Smooth-Swap: A Simple Enhancement for Face-Swapping with Smoothness
Abstrak
Face-swapping models have been drawing attention for their compelling generation quality, but their complex architectures and loss functions often require careful tuning for successful training. We propose a new face-swapping model called ‘Smooth-Swap’, which excludes complex handcrafted designs and allows fast and stable training. The main idea of Smooth-Swap is to build smooth identity embedding that can provide stable gradients for identity change. Unlike the one used in previous models trained for a purely discriminative task, the proposed embedding is trained with a supervised contrastive loss promoting a smoother space. With improved smoothness, Smooth-Swap suffices to be composed of a generic U-Net-based generator and three basic loss functions, a far simpler design compared with the previous models. Extensive experiments on face-swapping benchmarks (FFHQ, $Face-Forensics++$) and face images in the wild show that our model is also quantitatively and qualitatively comparable or even superior to the existing methods.
Topik & Kata Kunci
Penulis (3)
Jiseob Kim
Ji-Hyun Lee
Byoung-Tak Zhang
Akses Cepat
- Tahun Terbit
- 2021
- Bahasa
- en
- Total Sitasi
- 55×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/CVPR52688.2022.01051
- Akses
- Open Access ✓