Target Preserving Blending

Target-preserving blending focuses on merging different data sources—images, videos, or even model parameters—to create a unified output while minimizing distortions and preserving the essential features of the original inputs. Current research explores this across various domains, employing techniques like diffusion models, implicit neural representations (INRs), and transformer architectures to achieve seamless blending in applications such as image editing, video harmonization, and 3D face modeling. These advancements improve the quality and efficiency of various computer vision and graphics tasks, leading to more realistic and natural-looking results in diverse applications.