用局部低秩约束提升非刚性物体2D到3D重建精度
Unsupervised 2D-3D lifting of non-rigid objects using local constraints
- 在全局形状中局部应用低秩约束，平衡模型容量与约束力
- S-Up3D数据集上重建误差降低70%以上，超越现有方法
- 无需监督信号，适合无标注姿态-形状分离场景
对于非刚性物体，仅从2D关键点预测3D形状因遮挡及视角变化与形状变化难以区分而存在病态问题。以往方法常依赖专用模型中的低秩约束，但训练困难且限制重建质量。本文表明，采用无监督损失训练的通用高容量模型可实现更优重建。关键创新在于将低秩约束应用于全形状的局部子集，使模型在保持高表达能力的同时获得合理约束。在S-Up3D数据集上，重建误差相比当前最优方法降低超过70%。
原文摘要 · Abstract (English)
For non-rigid objects, predicting the 3D shape from 2D keypoint observations is ill-posed due to occlusions, and the need to disentangle changes in viewpoint and changes in shape. This challenge has often been addressed by embedding low-rank constraints into specialized models. These models can be hard to train, as they depend on finding a canonical way of aligning observations, before they can learn detailed geometry. These constraints have limited the reconstruction quality. We show that generic, high capacity models, trained with an unsupervised loss, allow for more accurate predicted shapes. In particular, applying low-rank constraints to localized subsets of the full shape allows the high capacity to be suitably constrained. We reduce the state-of-the-art reconstruction error on the S-Up3D dataset by over 70%.
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