无监督分离3D物体形状与形变，提升下游任务性能
DiLO: Disentangled Latent Optimization for Learning Shape and Deformation in Grouped Deforming 3D Objects
- 通过优化生成网络与形状/形变因子，实现无监督解耦表征
- 在人体、动物和表情数据集上表现优于或相当复杂方法
- 适用于形变迁移、分类与可解释性分析等场景
本文提出一种基于解耦潜在优化的方法，无需标注即可将分组变形的3D物体分解为形状与形变因子。方法联合优化生成网络及形状和形变因子，并引入特定正则化。第二阶段训练两个顺序无关的PoinNet编码器，实现高效近似推断。实验在3D人体、动物和面部表情数据集上验证了该方法在无监督形变迁移、形变分类和可解释性分析等下游任务中的有效性，性能媲美或超越复杂现有方法。
原文摘要 · Abstract (English)
In this work, we propose a disentangled latent optimization-based method for parameterizing grouped deforming 3D objects into shape and deformation factors in an unsupervised manner. Our approach involves the joint optimization of a generator network along with the shape and deformation factors, supported by specific regularization techniques. For efficient amortized inference of disentangled shape and deformation codes, we train two order-invariant PoinNet-based encoder networks in the second stage of our method. We demonstrate several significant downstream applications of our method, including unsupervised deformation transfer, deformation classification, and explainability analysis. Extensive experiments conducted on 3D human, animal, and facial expression datasets demonstrate that our simple approach is highly effective in these downstream tasks, comparable or superior to existing methods with much higher complexity.
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