In this paper, we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single-view 2D image sequence. In contrast to prior motion-based methods, which estimate relatively simple articulations, our method can generate arbitrarily complex kinematic structures with skeletal topology via a successive iterative merging strategy. The iterative merge process is guided by a density weighted skeleton map which is generated from a novel object boundary generation method from sparse 2D feature points. Our main contributions can be summarised as follows: (i) An unsupervised complex articulated kinematic structure estimation method that combines motion segments with skeleton information. (ii) An iterative fine-to-coarse merging strategy for adaptive motion segmentation and structural topology embedding. (iii) A skeleton estimation method based on a novel silhouette boundary generation from sparse feature points using an adaptive model selection method. (iv) A new highly articulated object dataset with ground truth annotation. We have verified the effectiveness of our proposed method in terms of computational time and estimation accuracy through rigorous experiments with multiple datasets. Our experiments show that the proposed method outperforms state-of-the-art methods both quantitatively and qualitatively.

中文翻译：

---

结合运动和骨骼信息的高度关节运动结构估计


在本文中，我们提出了一种新颖的框架，用于从单视图 2D 图像序列中学习复杂铰接物体的无监督运动结构。与之前估计相对简单的关节的基于运动的方法相比，我们的方法可以通过连续的迭代合并策略生成具有骨骼拓扑的任意复杂的运动结构。迭代合并过程由密度加权骨架图引导，该骨架图是根据稀疏 2D 特征点通过新颖的对象边界生成方法生成的。我们的主要贡献可以概括如下：（i）一种将运动段与骨架信息相结合的无监督复杂关节运动结构估计方法。 （ii）用于自适应运动分割和结构拓扑嵌入的迭代精细到粗略合并策略。 (iii)一种基于使用自适应模型选择方法从稀疏特征点生成新颖轮廓边界的骨架估计方法。 (iv) 具有地面实况注释的新的高度清晰的对象数据集。通过对多个数据集的严格实验，我们验证了我们提出的方法在计算时间和估计精度方面的有效性。我们的实验表明，所提出的方法在数量和质量上都优于最先进的方法。