{"type":"ScholarlyArticle","title":"BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion","authors":[{"name":"Yonghao Yu","slug":"yonghao-yu","given":"Yonghao","family":"Yu","affiliation":"Waseda University"},{"name":"Shunan Zhu","slug":"shunan-zhu","given":"Shunan","family":"Zhu","affiliation":"Waseda University"},{"name":"Huai Qin","slug":"huai-qin","given":"Huai","family":"Qin","affiliation":"Waseda University"},{"name":"Haorui Li","slug":"haorui-li","given":"Haorui","family":"Li","affiliation":"Southeast University"}],"abstract":"Witnessing the evolution of text-to-image diffusion models, significant strides have been made in text-to-3D generation. Currently, two primary paradigms dominate the field of text-to-3D: the feed-forward generation solutions, capable of swiftly producing 3D assets but often yielding coarse results, and the Score Distillation Sampling (SDS) based solutions, known for generating high-fidelity 3D assets albeit at a slower pace. The synergistic integration of these methods holds substantial promise for advancing 3D generation techniques. In this paper, we present BoostDream, a highly efficient plug-and-play 3D refining method designed to transform coarse 3D assets into high-quality. The BoostDream framework comprises three distinct processes: (1) We introduce 3D model distillation that fits differentiable representations from the 3D assets obtained through feed-forward generation. (2) A novel multi-view SDS loss is designed, which utilizes a multi-view aware 2D diffusion model to refine the 3D assets. (3) We propose to use prompt and multi-view consistent normal maps as guidance in refinement. Our extensive experiment is conducted on different differentiable 3D representations, revealing that BoostDream excels in generating high-quality 3D assets rapidly, overcoming the Janus problem compared to conventional SDS-based methods. This breakthrough signifies a substantial advancement in both the efficiency and quality of 3D generation processes.","doi":"10.24963/ijcai.2024/598","url":"https://ijcai.org/proceedings/2024/598","pdf":"https://ijcai.org/proceedings/2024/0598.pdf","markdown":"https://ijcai.org/proceedings/2024/598.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/598","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":"Machine Learning","track":"Main Track","number":598,"pages":{"first":5407,"last":5415},"keywords":["Multidisciplinary Topics and Applications: MTA: Arts and creativity","Machine Learning: ML: Generative models","Computer Vision: CV: 3D computer vision"],"videos":[],"references":[{"position":1,"raw":"[Balaji et al., 2022] Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, et al. ediffi: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324, 2022.","title":"ediffi: Text-to-image diffusion models with an ensemble of expert denoisers","authors":"Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2023a] Hansheng Chen, Jiatao Gu, Anpei Chen, Wei Tian, Zhuowen Tu, Lingjie Liu, and Hao Su. 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