3D Gaussian Splatting (3DGS) has made significant strides in scene representation and neural rendering, with intense efforts focused on adapting it for dynamic scenes. Despite delivering remarkable rendering quality and speed, existing methods struggle with storage demands and the representation of complex real-world motions. To address these challenges, we propose MoDec-GS, a memory-efficient Gaussian splatting framework designed to reconstruct novel views in challenging scenarios with complex motions. We introduce Global-to-Local Motion Decomposition (GLMD) to effectively capture dynamic motions in a coarse-to-fine manner. This approach leverages Global Canonical Scaffolds (Global CS) and Local Canonical Scaffolds (Local CS), which extend static Scaffold representation to dynamic video reconstruction. For Global CS, we propose Global Anchor Deformation (GAD) to efficiently represent global dynamics along complex motions, by directly deforming the implicit Scaffold attributes which are anchor position, offset, and local context features. Next, we finely adjust local motions via the Local Gaussian Deformation (LGD) of Local CS explicitly. Additionally, we introduce Temporal Interval Adjustment (TIA) to automatically control the temporal coverage of each Local CS during training, enabling MoDec-GS to find optimal interval assignments based on the specified number of temporal segments. Extensive evaluations demonstrate that MoDec-GS achieves an average 70% reduction in model size over state-of-the-art methods for dynamic 3D Gaussians from real-world dynamic videos while maintaining or even improving rendering quality.

A new approach for occlusion-robust 3D human mesh reconstruction from a single image is introduced in this paper. Since occlusion has emerged as a major problem to be resolved in this field, there have been meaningful efforts to deal with various types of occlusions (e.g., person-to-person occlusion, person-to-object occlusion, self-occlusion, etc.). Although many recent studies have shown the remarkable progress, previous regression-based methods still have respective limitations to handle occlusion problems due to the lack of the appearance information. To address this problem, we propose a novel method for human mesh reconstruction based on the pose-relevant subspace analysis. Specifically, we first generate a set of eigenvectors, so-called eigenposes, by conducting the singular value decomposition (SVD) of the pose matrix, which contains diverse poses sampled from the training set. These eigenposes are then linearly combined to construct a target body pose according to fusing coefficients, which are learned through the proposed network. Such combination of principal body postures (i.e., eigenposes) in a global manner gives a great help to cope with partial ambiguities by occlusions. Furthermore, we also propose to exploit a joint injection module that efficiently incorporates the spatial information of visible joints into the encoded feature during the estimation process of fusing coefficients. Experimental results on benchmark datasets demonstrate the ability of the proposed method to robustly reconstruct the human mesh under various occlusions occurring in real-world scenarios. The code and model are publicly available at: https://github.com/DCVL-3D/Eigenpose_release.

A simple yet effective method for occlusion-robust 3D human mesh reconstruction from a single image is presented in this paper. Although many recent studies have shown the remarkable improvement in human mesh reconstruction, it is still difficult to generate accurate meshes when person-to-person occlusion occurs due to the ambigu-ity of who a body part belongs to. To address this problem, we propose an instance-aware contrastive learning scheme. Specifically, joint features belonging to the target human are trained to be proximate with the center feature (i.e., feature extracted from the body center position). On the other hand, center features of different human instances are forced to be far apart so that joint features of each person can be clearly distinguished from others. By interpreting the joint possession based on such contrastive learning scheme, the proposed method easily understands the spatial occupancy of body parts for each person in a given image, thus can reconstruct reliable human meshes even with severely overlapped cases between multiple persons. Ex-perimental results on benchmark datasets demonstrate the robustness of the proposed method compared to previous approaches under person-to-person occlusions. The code and model are publicly available at: https://github.com/DCVL-3D/InstanceHMR_release.

This paper presents a novel structured camera pose estimation framework for mosaic-based omnidirectional imaging, i.e., producing a wide field of view (FoV) image that covers an entire sphere of the surroundings from a set of regular FoV images. With the effective utilization of geometric priors, the proposed framework exploits an individual image's connected structure while sequentially extracting correspondence between them. In the proposed framework, 2DSfM, a structure from motion method for multi-view images in structured 2D grids, is also proposed. Additionally, we propose a constraint term for rotation vectors in the bundle adjustment process that efficiently incorporates structural priors. We demonstrate our framework on structured omnidirectional image scenes and compare to existing frameworks. The experimental results show that our framework outperforms well-known conventional frameworks regarding both average reprojection error and reconstruction results.

This paper introduces an development of high-definition(HD) 3DTV broadcasting system including 3DTV encoder system and 3DTV receiver. This developed 3DTV broadcasting system employs an innovative 3DTV broadcasting technology, called as dual-stream method in which left and right images are encoded by using each video encoder and multiplexed using MPEG-2 system technology for guaranteeing backward-compatibility with legacy DTV. This paper specifically describes 3DTV re-multiplexer that is a component of 3DTV encoding system and that has functionality for synchronization of dual-stream 3DTV signal, followed by description of 3DTV receiver. Finally, implementation results will be summarized, introducing 3DTV trial service that uses the developed system.