View synthesis aims to produce unseen views from a set of views captured by two or more cameras at different positions. This task is non-trivial since it is hard to conduct pixel-level matching among different views. To address this issue, most existing methods seek to exploit the geometric information to match pixels. However, when the distinct cameras have a large baseline (i.e., far away from each other), severe geometry distortion issues would occur and the geometric information may fail to provide useful guidance, resulting in very blurry synthesized images. To address the above issues, in this paper, we propose a novel deep generative model, called Self-Consistent Generative Network (SCGN), which synthesizes novel views from the given input views without explicitly exploiting the geometric information. The proposed SCGN model consists of two main components, i.e., a View Synthesis Network (VSN) and a View Decomposition Network (VDN), both employing an Encoder-Decoder structure. Here, the VDN seeks to reconstruct input views from the synthesized novel view to preserve the consistency of view synthesis. Thanks to VDN, SCGN is able to synthesize novel views without using any geometric rectification before encoding, making it easier for both training and applications. Finally, adversarial loss is introduced to improve the photo-realism of novel views. Both qualitative and quantitative comparisons against several state-of-the-art methods on two benchmark tasks demonstrated the superiority of our approach.
Automatic cranial model deformation has a significant impact on the ergonomic design of headgears. It benefits product design by providing accurate human cranial measurements while automatically deforming to target shapes. With the development of automatic deformation methods, cranial modeling can now be handled efficiently rather than manually customized. Furthermore, previous studies have shown that integrating anatomical landmarks in deformation methods can improve modeling accuracy. Hence, this study provides anatomical definitions of cranial landmarks, including 51 skull landmarks and 14 mandible landmarks.This study compares three different landmark-guided deformation methods using the above anatomic landmarks, including Landmark-Guided Coherent Point Drift (LGCPD), Neural Deformation Pyramid (NDP), and the registration part in SCULPTOR (S-ARAP). These three methods treat the automatic deformation problem as a task of probability density estimation, hierarchical deformation decomposition, and local rigidity preservation, respectively. However, LGCPD is computationally intensive, which means once the cranial model has many vertices, the computation consumes a large memory and runs slowly. Additionally, LGCPD is sensitive to obtain a suboptimal solution and results in a deformed model with a high shape variation. NDP simplifies the deformation problem by decomposing it into several sub-deformations. With Multi-Layer Perception (MLP), NDP can perform the deformation approximately 10 times faster than LGCPD. However, without the constraint of local rigidity, partial-to-partial deformation accumulates minor deformation errors from each sub-step, leading to unsatisfactory deformation results. S-ARAP uniformly samples control nodes and computes their influence weights on the source model's vertices using Radial Basis Function (RBF). The larger the distance between the node and the vertices, the higher the weight with a stronger influence. The as-rigid-as-possible (ARAP) term is then introduced to preserve the local rigidity of the deformed model with the calculated influence weights for the local regions. Therefore, S-ARAP can automatically deform the cranial model, particularly the skull part with complex geometries, to achieve a well-structured result. Moreover, the control node sampling speeds up the execution of deformation while using less memory than LGCPD. Instead of the decomposition in NDP, S-ARAP increases the number of control nodes in several stages to perform hierarchical deformation.Finally, with quantitative and qualitative experimental results, the study discusses and compares the suitability of these three deformation methods for automatic cranial modeling. The study computes Chamfer-Distance (CD) and Point-to-Plane Distance (PTPD) on the deformed results for quantitative comparisons. CD determines the distance between deformed vertices and the nearest vertices on the target model and vice versa. PTPD calculates the distance between the deformed vertices and the nearest plane on the target model to calculate the shape error. The maximum value in PTPD can help identify outliers in deformed results. Lower CD and PTPD values suggest a better match with the target. According to the experimental results, S-ARAP outperforms LGCPD and NDP in terms of CD and PTPD. Furthermore, the deformed data are visualized with a heatmap revealing the large deformation error, and S-ARAP shows the lowest fitting error on the deformed results. Thus, S-ARAP is a suitable method for automatic deformation on cranial modeling.
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