Image Semantic Transformation: Faster, Lighter and Stronger
This work addresses the need for efficient and versatile image editing tools, though it appears incremental as it builds on existing FaceNet technology.
The paper tackles the problem of performing high-level semantic transformations on images, such as changing gender or adding a smile, by proposing the ISTRC model which uses FaceNet's Euclidean latent space to reconstruct images and manipulate latent vectors, achieving 10 different transformations with training times of 3 hours on a GTX 1080.
We propose Image-Semantic-Transformation-Reconstruction-Circle(ISTRC) model, a novel and powerful method using facenet's Euclidean latent space to understand the images. As the name suggests, ISTRC construct the circle, able to perfectly reconstruct images. One powerful Euclidean latent space embedded in ISTRC is FaceNet's last layer with the power of distinguishing and understanding images. Our model will reconstruct the images and manipulate Euclidean latent vectors to achieve semantic transformations and semantic images arthimetic calculations. In this paper, we show that ISTRC performs 10 high-level semantic transformations like "Male and female","add smile","open mouth", "deduct beard or add mustache", "bigger/smaller nose", "make older and younger", "bigger lips", "bigger eyes", "bigger/smaller mouths" and "more attractive". It just takes 3 hours(GTX 1080) to train the models of 10 semantic transformations.