Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟰𝟱𝟲 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing by Shanghai AI Laboratory Follow me for a similar post: Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 This paper is published cvpr2022. 🔸 Github: https://lnkd.in/grcbZc6u ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 ➡️ Recent advances like StyleGAN have promoted the growth of controllable facial editing. ➡️ To address its core challenge of attribute decoupling in a single latent space, attempts have been made to adopt dual-space GAN for better disentanglement of style and content representations. ➡️ Nonetheless, these methods are still incompetent to obtain plausible editing results with high controllability, especially for complicated attributes. ➡️ In this study, we highlight the importance of interaction in a dual-space GAN for more controllable editing. We propose TransEditor, a novel Transformer-based framework to enhance such interaction. ➡️ Besides, we develop a new dual-space editing and inversion strategy to provide additional editing flexibility. ➡️ Extensive experiments demonstrate the superiority of the proposed framework in image quality and editing capability, suggesting the effectiveness of TransEditor for highly controllable facial editing. #computervision #artificialintelligence #deeplearning