用ARAP正则化提升扩散模型对变形形状的生成能力
ARAPDiffusion: ARAP Regularization for Diffusion-Based Deformable Shape Space Learning
- 将ARAP刚性保持模型融入扩散模型，作为潜在空间正则化
- 仅需少量3D数据即可学习连续形状空间，生成效果优于基线
- 适合处理无组织点云的形状生成，无需显式网格表示
本文提出ARAPDiffusion，一种基于潜在扩散模型的变形形状空间学习方法。核心创新在于将逐刚性可能（ARAP）变形模型作为正则化损失注入潜在扩散模型，从而在无需大量3D训练数据的情况下学习生成模型。与标准潜在扩散模型不同，本方法通过交替优化：利用扩散模型生成的合成分布构建增强形状编码器/解码器的正则化损失，再利用形状解码器构建改进扩散模型的正则化损失。实验表明，该方法在无条件与条件形状生成任务中均优于基线。同时展示了潜在扩散范式在无显式表示下的优势，结合隐式形状解码器，可直接处理无组织点云。
原文摘要 · Abstract (English)
This paper introduces ARAPDiffusion, a latent diffusion model to learn the underlying continuous shape space of a deformation shape collection. The key innovation is in injecting the as-rigid-as-possible (ARAP) deformation model as regularization losses into latent diffusion (LD), releasing the requirement of having abundant 3D training data for learning generative models. In contrast to the standard LD, we show how the ARAP model can be used to improve both the encoder/decoder and the LD model. The training procedure alternates between using the synthetic distribution defined by the LD model to develop a regularization loss that enhances the shape encoder/decoder and using the shape decoder to develop a regularization loss to improve the LD model. We also show the benefit of the LD paradigm in combining a representation-free LD process and an implicit shape decoder that is applicable to unorganized point clouds. The experimental results of unconditional and conditional shape generation demonstrate the advantages of ARAPDiffusion over baseline approaches.
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