用少量数据实现单张深度图的非刚性3D姿态重建
Canonical Pose Reconstruction from Single Depth Image for 3D Non-rigid Pose Recovery on Limited Datasets
- 将变形物体映射到标准姿态，再恢复原始形态
- 仅用约300样本即超越现有最佳方法
- 适合小样本下人体动物3D重建任务
从2D输入进行3D重建，尤其是人类等非刚性物体，因形变范围大而面临挑战。传统方法在处理非刚性形状时通常需要大量训练数据以覆盖全部形变空间。本文提出一种标准姿态重建模型，可将单视角深度图中的可变形形状转换为标准形式，从而利用刚体重建技术完成形状重建，并在体素表示中同时恢复输入姿态。该方法结合原始与变形深度图，仅需约300个样本即可取得优异效果。在动物和人体数据集上的实验表明，本方法优于其他最先进的方法。
原文摘要 · Abstract (English)
3D reconstruction from 2D inputs, especially for non-rigid objects like humans, presents unique challenges due to the significant range of possible deformations. Traditional methods often struggle with non-rigid shapes, which require extensive training data to cover the entire deformation space. This study addresses these limitations by proposing a canonical pose reconstruction model that transforms single-view depth images of deformable shapes into a canonical form. This alignment facilitates shape reconstruction by enabling the application of rigid object reconstruction techniques, and supports recovering the input pose in voxel representation as part of the reconstruction task, utilizing both the original and deformed depth images. Notably, our model achieves effective results with only a small dataset of approximately 300 samples. Experimental results on animal and human datasets demonstrate that our model outperforms other state-of-the-art methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。