Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation
Publication in refereed journal

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其它資訊
摘要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 compact wavelet representation 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 generator to produce diverse shapes in the form of the coarse coefficient volumes and a detail predictor to produce compatible detail coefficient volumes for introducing fine structures and details. Further, we may jointly train an encoder network 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.
著者Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Ruihui Li, Chi-Wing Fu
期刊名稱ACM Transactions on Graphics
詳細描述Jingyu Hu^, Ka-Hei Hui^, Zhengzhe Liu, Ruihui Li, and Chi-Wing Fu (^ joint 1st authors)
出版年份2024
月份4
卷號43
期次2
出版社ACM
文章號碼16
頁次1 - 18
國際標準期刊號0730-0301
語言美式英語

上次更新時間 2024-15-10 於 09:59