In this paper we present a novel approach for real-time elastic object simulation. In our framework, an elastic object is represented by a hybrid model that couples metaballs which are distributed adaptively and a triangular surface mesh. We use the centers of metaballs as physical particles and apply Projective Dynamics for deformation simulation. To produce more realistic simulation, we propose a new Projective Dynamics constraint for metaballs that governs the elastic behavior using the deformation gradient approximation for each metaball. The metaballs generated by the self-adaptive method can better maintain the geometric feature details of the model, which enhance the model resolution in surface area. Furthermore, we propose a GPU based surface skinning method for the coupling of the triangular mesh and the metaballs. Our skinning method integrates the skinning process into the rendering pipeline and enables fast skinning of large-scale surface meshes. The experimental results show that the proposed method can obtain more plausible visual effect while achieving real-time deformation of 3D models.
3D human pose and shape estimation from monocular images is a fundamental task in computer vision, but it is highly ill-posed and challenging due to occlusion. Occlusion can be caused by other objects that block parts of the body from being visible in the image. When an occlusion occurs, the image features become incomplete and ambiguous, leading to inaccurate or even wrong predictions. In this paper, we propose a novel method, named RAGT, that can handle occlusion robustly and recover the complete 3D pose and shape of humans. Our study focuses on achieving robust feature representation for human pose and shape estimation in the presence of occlusion. To this end, we introduce a dual-branch architecture that learns incorporation weights from visible parts to occluded parts and suppression weights to inhibit the integration of background features. To further improve the quality of visible and occluded maps, we leverage pseudo ground-truth maps generated by DensePose for pixel-level supervision. Additionally, we propose a novel transformer-based module called COAT (Contextual Occlusion-Aware Transformer) to effectively incorporate visible features into occluded regions. The COAT module is guided by an Occlusion-Guided Attention Loss (OGAL). OGAL is designed to explicitly encourage the COAT module to fuse more important and relevant features that are semantically and spatially closer to the occluded regions. We conduct experiments on various benchmarks and prove the robustness of RAGT to the different kinds of occluded scenes both quantitatively and qualitatively.
The localization accuracy and robustness of visual odometry systems for static scenes can be significantly degraded in complex real-world environments with moving objects. This paper addresses the problem by proposing a semi-direct sparse visual odometry (SDSO) method designed for dynamic scenes. With the aid of the pixel-level semantic information, the system can not only eliminate dynamic points but also construct more accurate photometric errors for subsequent optimization. To obtain an accurate and robust camera pose in dynamic scenes, we propose a dual error optimization strategy that minimizes the reprojection and photometric errors consecutively. The proposed method has been extensively evaluated on the public datasets like the TUM dynamic dataset and KITTI dataset. The results demonstrate the effectiveness of our method in terms of localization accuracy and robustness compared with both the original direct sparse odometry (DSO) method and state-of-the-art methods for dynamic scenes.
Multi-material fused filament fabrication (FFF) offers the ability to print 3D objects with very diverse surface appearances. However, control of the surface appearance is largely a matter of trial and error unless the employed materials are very similar and very translucent, so we can think of them as blending together. When the multiple materials are fused into one filament in a diamond hotend extruder but do not blend, the resulting surface appearance depends on the printing direction. We explore how this leads to milli-scale colorations as a function of the printing direction. By having preferable printing directions, it is possible to exploit the limited color blending of this nozzle with multiple inlets and one outlet and further enhance particular color effects, such as goniochromatism. We present a framework based on both experimental and computational fluid dynamics analysis for controlling the extrusion process and the coloration of the surface according to preferable printing directions and mixing ratios with the aim of enabling fused filament fabrication of intricate surface appearances.
Reconstructing 3D human shapes with high-quality geometry as well as dense correspondences is important for many applications. Template fitting based methods can generate meshes with desired requirements but have difficulty in capturing high-quality details and accurate poses. The main challenge lies in the models have apparent discrepancies in different poses. Directly learning large-scale displacement of each point to account for different posed shapes is prone to artifacts and does not generalize well. Statistic representation based methods, can avoid artifacts by restricting human shapes to a limited shape expression space, which also makes it difficult to produce shape details. In this work, we propose a coarse-to-fine method to address the problem by dividing it into part approximation and shape refinement in an unsupervised manner. Our basic observation is that the poses of human parts account for most articulated shape variations and benefit pose generalization. Moreover, geometry details can be easily fitted once the part poses are estimated. At the coarse-fitting stage, we propose a part approximation network, to transform a template to fit inputs by a set of pose parameters. For refinement, we propose a shape refinement network, to fit shape details. Qualitative and quantitative studies on several datasets demonstrate that our method performs better than other unsupervised methods.
