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Graph."],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>This paper presents a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet domain. Specifically, we propose a<jats:italic>compact wavelet representation<\/jats:italic>with a pair of coarse and detail coefficient volumes to implicitly represent 3D shapes via truncated signed distance functions and multi-scale biorthogonal wavelets. Then, we design a pair of neural networks: a diffusion-based<jats:italic>generator<\/jats:italic>to produce diverse shapes in the form of the coarse coefficient volumes and a<jats:italic>detail predictor<\/jats:italic>to produce compatible detail coefficient volumes for introducing fine structures and details. Further, we may jointly train an<jats:italic>encoder network<\/jats:italic>to learn a latent space for inverting shapes, allowing us to enable a rich variety of whole-shape and region-aware shape manipulations. Both quantitative and qualitative experimental results manifest the compelling shape generation, inversion, and manipulation capabilities of our approach over the state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3635304","type":"journal-article","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T11:48:55Z","timestamp":1701431335000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4235-8679","authenticated-orcid":false,"given":"Jingyu","family":"Hu","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, HK SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9786-2648","authenticated-orcid":false,"given":"Ka-Hei","family":"Hui","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, HK SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8434-3224","authenticated-orcid":false,"given":"Zhengzhe","family":"Liu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, HK SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4266-6420","authenticated-orcid":false,"given":"Ruihui","family":"Li","sequence":"additional","affiliation":[{"name":"Hunan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5238-593X","authenticated-orcid":false,"given":"Chi-Wing","family":"Fu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, HK SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,1,3]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447648"},{"key":"e_1_3_2_3_1","first-page":"40","volume-title":"Proceedings of International Conference on Machine Learning (ICML)","author":"Achlioptas Panos","year":"2018","unstructured":"Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas J. 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