Year: 2025
Type: conference-paper
Abstract: We introduce an industrial Head Blending pipeline for the task of seamlessly integrating an actor's head onto a target body in digital content creation. The key challenge stems from discrepancies in head shape and hair structure, which lead to unnatural boundaries and blending artifacts. Existing methods treat foreground and back-ground as a single task, resulting in suboptimal blending quality. To address this problem, we propose CHANGER, a novel pipeline that decouples background integration from foreground blending. By utilizing chroma keying for artifact-free background generation and introducing Head shape and long Hair augmentation (H2augmen-tation) to simulate a wide range of head shapes and hair styles, CHANGER improves generalization on innumerable various real-world cases. Furthermore, our Fore-ground Predictive Attention Transformer (FPAT) module enhances foreground blending by predicting and focusing on key head and body regions. Quantitative and qualitative evaluations on benchmark datasets demonstrate that our CHANGER outperforms state-of-the-art methods, de-livering high-fidelity, industrial-grade results.
Institutions: Samsung (South Korea), Kyung Hee University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Indian Institute of Technology, Patna, National Research Foundation, Kyung Hee University, Ministry of Trade, Industry and Energy, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Korea Evaluation Institute of Industrial Technology, Institute for Information and Communications Technology Promotion
OpenAlex ID: W4409262420