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Unlike previous refined methods based on Score Distillation Sampling (SDS), which suffer from unstable optimization and geometric over-smoothing due to ambiguities across different views and modalities, our method effectively distills consistent multi-view and multi-modal priors from 2D diffusion models in a training-free manner. This enables robust optimization of 3D representations. Additionally, because SDD is training-free, it preserves the diffusion\u2019s prior knowledge and mitigates potential degradation. This characteristic makes it highly compatible with advanced 2D diffusion techniques like IP-Adapters and ControlNet, allowing for more controllable 3D generation with additional conditioning signals. Experiments demonstrate that our method produces high-quality 3D results with plausible textures and intricate geometric details.\n          <\/jats:p>","DOI":"10.1145\/3759249","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:13:46Z","timestamp":1754565226000},"page":"1-13","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Iris3D: 3D Generation via Synchronized Diffusion Distillation"],"prefix":"10.1145","volume":"45","author":[{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-4750-8875","authenticated-orcid":false,"given":"Yixun","family":"Liang","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology","place":["Hong Kong, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0009-0009-0465-0581","authenticated-orcid":false,"given":"Weiyu","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology","place":["Hong Kong, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0009-0003-7122-5207","authenticated-orcid":false,"given":"Rui","family":"Chen","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology","place":["Hong Kong, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-8105-5836","authenticated-orcid":false,"given":"Fei-Peng","family":"Tian","sequence":"additional","affiliation":[{"name":"Light Illusions","place":["Shenzhen, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0009-0004-0956-7596","authenticated-orcid":false,"given":"Jiarui","family":"Liu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology","place":["Hong Kong, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-9565-8205","authenticated-orcid":false,"given":"Ying-Cong","family":"Chen","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology - Guangzhou Campus","place":["Guangzhou, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-4506-6973","authenticated-orcid":false,"given":"Ping","family":"Tan","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology","place":["Hong Kong, China"]}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-3386-8805","authenticated-orcid":false,"given":"Xiao-Xiao","family":"Long","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology","place":["Hong Kong, China"]},{"name":"Nanjing University","place":["Hong Kong, China"]}]}],"member":"320","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"e_1_3_3_2_1","unstructured":"Mohammadreza Armandpour Ali Sadeghian Huangjie Zheng Amir Sadeghian and Mingyuan Zhou. 2023. 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