Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟰𝟵𝟳 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Few-Shot Head Swapping in the Wild by Baidu Inc. Follow me for a similar post: Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 This paper is published in ARXIV2022. 🤝 Propose the Head Swapper (Heser), which achieves few-shot head swapping for in-the-wild scenarios. ➡️ Specifically, our Head2Head Aligner generates high-fidelity reenact results with high pose and expression consistency, and the Head2Scene Blender seamlessly blends the aligned source head to the target image while maintaining the color of the target person. Extensive experiments demonstrate that the HeSer can achieve superior head swapping results on a variety of scenes. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 👉 The head swapping task aims at flawlessly placing a source head onto a target body, which is of great importance to various entertainment scenarios. 👉 While face swapping has drawn much attention, the task of head swapping has rarely been explored, particularly under the few-shot setting. 👉 It is inherently challenging due to its unique needs in head modeling and background blending. 👉 In this paper, we present the Head Swapper (HeSer), which achieves few-shot head swapping in the wild through two delicately designed modules. 👉 Firstly, a Head2Head Aligner is devised to holistically migrate pose and expression information from the target to the source head by examining multi-scale information. 👉 Secondly, to tackle the challenges of skin color variations and head-background mismatches in the swapping procedure, a Head2Scene Blender is introduced to simultaneously modify facial skin color and fill mismatched gaps on the background around the head. 👉 Particularly, seamless blending is achieved with the help of a Semantic-Guided Color Reference Creation procedure and a Blending UNet. 👉 Extensive experiments demonstrate that the proposed method produces superior head swapping results on a variety of scenes. #computervision #artificialintelligence  #deeplearning #data #technology