Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation

• URL: http://arxiv.org/abs/2302.00190v1
• Date: Wed, 1 Feb 2023 02:47:53 GMT
• Title: Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation
• Authors: Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Ruihui Li and Chi-Wing Fu
• Abstract summary: We present 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. 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.
• Score: 54.09274684734721
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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