DiLO：面向分组变形3D对象学习形状与变形的解耦隐式优化方法
DiLO: Disentangled Latent Optimization for Learning Shape and Deformation in Grouped Deforming 3D Objects
AI总结：
本文提出DiLO方法，通过解耦隐式优化将分组变形3D对象无监督地分解为形状与变形因子，并利用双PointNet编码器实现高效推断，在变形迁移、分类等任务上以较低复杂度达到或超越现有方法。
摘要：
在这项工作中，我们提出了一种基于解耦隐式优化的方法，以无监督方式将分组变形的3D对象参数化为形状因子和变形因子。我们的方法涉及生成器网络与形状因子、变形因子的联合优化，并辅以特定的正则化技术。为了对解耦的形状和变形编码进行高效的摊销推断，我们在方法的第二阶段训练了两个阶不变（order-invariant）的基于PointNet的编码器网络。我们展示了该方法的多个重要下游应用，包括无监督变形迁移、变形分类和可解释性分析。在3D人体、动物和面部表情数据集上进行的大量实验表明，我们的简单方法在这些下游任务中非常有效，与复杂度高得多的现有方法相比具有可比性或更优的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
