Computers & Graphics-Uk最新文献

The primary aim of image-based virtual try-on is to seamlessly deform the target garment image to align with the human body. Owing to the inherent non-rigid nature of garments, current methods prioritise flexible deformation through appearance flow with high degrees of freedom. However, existing appearance flow estimation methods solely focus on the correlation of local feature information. While this strategy successfully avoids the extensive computational effort associated with the direct computation of the global information correlation of feature maps, it leads to challenges in garments adapting to large deformation scenarios. To overcome these limitations, we propose the GIC-Flow framework, which obtains appearance flow by calculating the global information correlation while reducing computational regression. Specifically, our proposed global streak information matching module is designed to decompose the appearance flow into horizontal and vertical vectors, effectively propagating global information in both directions. This innovative approach considerably diminishes computational requirements, contributing to an enhanced and efficient process. In addition, to ensure the accurate deformation of local texture in garments, we propose the local aggregate information matching module to aggregate information from the nearest neighbours before computing the global correlation and to enhance weak semantic information. Comprehensive experiments conducted using our method on the VITON and VITON-HD datasets show that GIC-Flow outperforms existing state-of-the-art algorithms, particularly in cases involving complex garment deformation.

Surface reconstruction for point clouds is a central task in 3D modeling. Recently, the attractive approaches solve this problem by learning neural implicit representations, e.g., unsigned distance functions (UDFs), from point clouds, which have achieved good performance. However, the existing UDF-based methods still struggle to recover the local geometrical details. One of the difficulties arises from the used inflexible representations, which is hard to capture the local high-fidelity geometry details. In this paper, we propose a novel neural implicit representation, named MuSic-UDF, which leverages Multi-Scale dynamic grids for high-fidelity and flexible surface reconstruction from raw point clouds with arbitrary typologies. Specifically, we initialize a hierarchical voxel grid where each grid point stores a learnable 3D coordinate. Then, we optimize these grids such that different levels of geometry structures can be captured adaptively. To further explore the geometry details, we introduce a frequency encoding strategy to hierarchically encode these coordinates. MuSic-UDF does not require any supervisions like ground truth distance values or point normals. We conduct comprehensive experiments under widely-used benchmarks, where the results demonstrate the superior performance of our proposed method compared to the state-of-the-art methods.

In various situations, such as clinical environments with sterile conditions or when hands are occupied with multiple devices, traditional methods of navigation and scene adjustment are impractical or even impossible. We explore a new solution by using voice control to facilitate interaction in virtual worlds to avoid the use of additional controllers. Therefore, we investigate three scenarios: Object Orientation, Visualization Customization, and Analytical Tasks and evaluate whether natural language interaction is possible and promising in each of these scenarios. In our quantitative user study participants were able to control virtual environments effortlessly using verbal instructions. This resulted in rapid orientation adjustments, adaptive visual aids, and accurate data analysis. In addition, user satisfaction and usability surveys showed consistently high levels of acceptance and ease of use. In conclusion, our study shows that the use of natural language can be a promising alternative for the improvement of user interaction in virtual environments. It enables intuitive interactions in virtual spaces, especially in situations where traditional controls have limitations.

Aortic dissection is a rare disease affecting the aortic wall layers splitting the aortic lumen into two flow channels: the true and false lumen. The rarity of the disease leads to a sparsity of available datasets resulting in a low amount of available training data for in-silico studies or the training of machine learning algorithms. To mitigate this issue, we use statistical shape modeling to create a database of Stanford type B dissection surface meshes. We account for the complex disease anatomy by modeling two separate flow channels in the aorta, the true and false lumen. Former approaches mainly modeled the aortic arch including its branches but not two separate flow channels inside the aorta. To our knowledge, our approach is the first to attempt generating synthetic aortic dissection surface meshes. For the statistical shape model, the aorta is parameterized using the centerlines of the respective lumen and the according ellipses describing the cross-section of the lumen while being aligned along the centerline employing rotation-minimizing frames. To evaluate our approach we introduce disease-specific quality criteria by investigating the torsion and twist of the true lumen.

The success of a structure preserving filtering technique has relied on its capability to recognize structures and textures present in the input image. In this paper a novel structure preserving filtering technique is presented that first, generates an edge-map of the input image by exploiting semantic information. Then, an edge-aware adaptive recursive median filter is utilized to produce the filter image. The technique provides satisfactory results for a wide variety of images with minimal fine-tuning of its parameters. Moreover, along with the various computer graphics applications the proposed technique also shows its robustness to incorporate spatial information for spectral-spatial classification of hyperspectral images. A MATLAB implementation of the proposed technique is available at-https://www.github.com/K-Pradhan/A-semantic-edge-aware-parameter-efficient-image-filtering-technique

Software testing is a vital tool to ensure the quality and trustworthiness of the pieces of software produced. Test suites are often large, which makes the process of testing software a costly and time-consuming process. In this context, test case prioritization (TCP) methods play an important role by ranking test cases in order to enable early fault detection and, hence, enable quicker problem fixes. The evaluation of such methods is a difficult problem, due to the variety of the methods and objectives. To address this issue, we present TPVis, a visual analytics framework that enables the evaluation and comparison of TCP methods designed in collaboration with experts in software testing. Our solution is an open-source web application that provides a variety of analytical tools to assist in the exploration of test suites and prioritization algorithms. Furthermore, TPVis also provides dashboard presets, that were validated with our domain collaborators, that support common analysis goals. We illustrate the usefulness of TPVis through a series of use cases that illustrate our system’s flexibility in addressing different problems in analyzing TCP methods. Finally, we also report on feedback received from the domain experts that indicate the effectiveness of TPVis. TPVis is available at https://github.com/vixe-cin-ufpe/TPVis.