{"type":"ScholarlyArticle","title":"Integrating View Conditions for Image Synthesis","authors":[{"name":"Jinbin Bai","slug":"jinbin-bai","given":"Jinbin","family":"Bai","affiliation":""},{"name":"Zhen Dong","slug":"zhen-dong","given":"Zhen","family":"Dong","affiliation":"University of California , Berkeley"},{"name":"Aosong Feng","slug":"aosong-feng","given":"Aosong","family":"Feng","affiliation":"Yale Univeristy"},{"name":"Xiao Zhang","slug":"xiao-zhang","given":"Xiao","family":"Zhang","affiliation":""},{"name":"Tian Ye","slug":"tian-ye","given":"Tian","family":"Ye","affiliation":"The Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Kaicheng Zhou","slug":"kaicheng-zhou","given":"Kaicheng","family":"Zhou","affiliation":""}],"abstract":"In the field of image processing, applying intricate semantic modifications within existing images remains an enduring challenge. This paper introduces a pioneering framework that integrates viewpoint information to enhance the control of image editing tasks, especially for interior design scenes. By surveying existing object editing methodologies, we distill three essential criteria --- consistency, controllability, and harmony --- that should be met for an image editing method. In contrast to previous approaches, our framework takes the lead in satisfying all three requirements for addressing the challenge of image synthesis. Through comprehensive experiments, encompassing both quantitative assessments and qualitative comparisons with contemporary state-of-the-art methods, we present compelling evidence of our framework's superior performance across multiple dimensions. This work establishes a promising avenue for advancing image synthesis techniques and empowering precise object modifications while preserving the visual coherence of the entire composition.","doi":"10.24963/ijcai.2024/840","url":"https://ijcai.org/proceedings/2024/840","pdf":"https://ijcai.org/proceedings/2024/0840.pdf","markdown":"https://ijcai.org/proceedings/2024/840.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/840","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI, Arts & Creativity","track":"AI, Arts & Creativity","number":840,"pages":{"first":7591,"last":7599},"keywords":["Application domains: Images, movies and visual arts"],"videos":[],"references":[{"position":1,"raw":"[Achiam et al., 2023] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Alt- man, Shyamal Anadkat, et al. 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