Abstract
We present a cascaded diffusion model based on a part-level implicit 3D representation. Our model achieves state-of-the-art generation quality and also enables part-level shape editing and manipulation without any additional training in conditional setup. Diffusion models have demonstrated impressive capabilities in data generation as well as zero-shot completion and editing via a guided reverse process. Recent research on 3D diffusion models has focused on improving their generation capabilities with various data representations, while the absence of structural information has limited their capability in completion and editing tasks. We thus propose our novel diffusion model using a part-level implicit representation. To effectively learn diffusion with high-dimensional embedding vectors of parts, we propose a cascaded framework, learning diffusion first on a low-dimensional subspace encoding extrinsic parameters of parts and then on the other high-dimensional subspace encoding intrinsic attributes. In the experiments, we demonstrate the outperformance of our method compared with the previous ones both in generation and part-level completion and manipulation tasks.
Abstract (translated)
我们提出了基于零件级别的隐含三维表示的级联扩散模型。我们的模型实现了最先进的生成质量,并在条件设置下无需额外的训练即可实现零件级别的形状编辑和操纵。扩散模型通过引导逆过程展示了在数据生成和零次完成和编辑任务方面令人印象深刻的能力。最近的3D扩散模型研究主要关注通过多种数据表示来提高生成能力,而缺乏结构信息则限制了完成和编辑任务的能力。因此我们提出了我们的新型扩散模型,使用零件级别的高维嵌入向量来学习扩散。为了有效地学习由零件级别的高维嵌入向量学习的扩散,我们提出了级联框架。我们首先学习零件外部参数的低维子空间,然后学习另一个高维子空间以学习内部属性。在实验中,我们证明了我们方法相比之前方法在生成和零件级别完成和操纵任务方面的表现优异。
URL
https://arxiv.org/abs/2303.12236
