119 research outputs found
Metric representations for shape analysis and synthesis
No full text2D and 3D geometric shapes are ubiquitous in computer graphics, computer animation, and computer-aided design and manufacturing. Two of the fundamental research challenges that underline these applications are the analysis and synthesis of shapes, with the former aiming to extract semantically meaningful knowledge of shapes and the latter focusing on generating plausible-looking shapes based on user inputs. Traditionally, shape analysis and synthesis are based on representations such as meshes, parameterisations, and Laplacians, which lead to mostly hand-crafted computation rules that are either suboptimal or treat related tasks separately. In this work, we propose to represent a 2D/3D shape as a square symmetric matrix that correlates every pair of geometric points on the shape, which allows us to formulate shape analysis and synthesis problems as principled optimisation problems that can be globally optimised. To demonstrate the usefulness of our new metric representation for shape analysis, we first address 3D mesh saliency detection by representing a shape as a pairwise feature distance matrix, whose principal eigenvector is experimentally shown to outperform the traditional saliency detection rules for capturing ground truth saliency annotations. Following this work, we then unify saliency detection and nonrigid shape matching via a jointly learned metric representation, which is shown to improve the accuracy of both tasks on the existing saliency detection and shape matching benchmarks. To also demonstrate the usefulness of our metric representation for shape synthesis, we address 2D facial shape beautification in images by representing a facial shape as the orthogonal projection matrix onto 2D facial landmarks, which is shown to improve the attractiveness of both frontal-neutral and non-frontal-non-neutral faces in the user studies. Finally, we show that adversarially learning the distributions of human shapes and poses in a hidden space produces higher quality human samples than in the geometry space. Together, these results show that our metric representation benefits both the analysis and synthesis of shapes, with the potential of unifying more diverse tasks such as part segmentation and labelling in the future work
Integrating spare part inventory management and predictive maintenance as a digital supply chain solution
No full textPurpose-The present study aims to assess the feasibility and effectiveness of incorporating predictive maintenance (PdM) into existing practices of spare part inventory management and pinpoint the barriers and identify economic values for such integration within the supply chain (SC). Design/methodology/approach-A two-staged embedded multiple case study with multi-method data collection and a combined discrete/continuous simulation were conducted to diagnose obstacles and recommend a potential solution. Findings-Several major organisational, infrastructure and cultural obstacles were revealed and an optimum scenario for the integration of spare part inventory management with PdM was recommended. Practical implications-The proposed solution can significantly decrease the inventory and SC costs as well as machinery downtimes through minimising unplanned maintenance and address shortage of spare parts. Originality-This is the first study with the best of our knowledge that offers further insights for practitioners in the Industry 4.0 (I4.0) era looking into embarking on digital integration of PdM and spare part inventory management as an efficient and resilient SC practice for the automotive sector by providing empirical evidence.Deposit licences Emerald allows authors to deposit their AAM under the Creative Commons Attribution Non-commercial International Licence 4.0 (CC BY-NC 4.0). To do this, the deposit must clearly state that the AAM is deposited under this licence and that any reuse is allowed in accordance with the terms outlined by the licence. To reuse the AAM for commercial purposes, permission should be sought by contacting [email protected]
DSPP: Deep Shape and Pose Priors of Humans
No full textThe prior knowledge of real human body shapes and poses is fundamentalin computer games and animation (e.g. performance capture). Linear subspaces such as the popular SMPL model have a limited capacity to represent the large geometric variations of human shapes and poses. What is worse is that random sampling from them often produces non-realistic humans because the distribution of real humans is more likely to concentrate on a non-linear manifold instead of the full subspace. Towards this problem, we propose to learn human shape and pose manifolds using a more powerful deep generator network, which is trained to produce samples that cannot be distinguished from real humans by a deep discriminator network. In contrast to previous work that learn both the generator and discriminator in the original geometry spaces, we learn them in the more representative latent spaces discovered by a shape and a pose auto-encoder network respectively. Random sampling from our priors produces higher-quality human shapes and poses. The capacity of our priors is best applied to applications such as virtual human synthesis in games
TED-Face: Texture-Enhanced Deep Face Reconstruction in the Wild
No full textWe present TED-Face, a new method for recovering high-fidelity 3D facial geometry and appearance with enhanced textures from single-view images. While vision-based face reconstruction has received intensive research in the past decades due to its broad applications, it remains a challenging problem because human eyes are particularly sensitive to numerically minute yet perceptually significant details. Previous methods that seek to minimize reconstruction errors within a low-dimensional face space can suffer from this issue and generate close yet low-fidelity approximations. The loss of high-frequency texture details is a key factor in their process, which we propose to address by learning to recover both dense radiance residuals and sparse facial texture features from a single image, in addition to the variables solved by previous work—shape, appearance, illumination, and camera. We integrate the estimation of all these factors in a single unified deep neural network and train it on several popular face reconstruction datasets. We also introduce two new metrics, visual fidelity (VIF) and structural similarity (SSIM), to compensate for the fact that reconstruction error is not a consistent perceptual metric of quality. On the popular FaceWarehouse facial reconstruction benchmark, our proposed system achieves a VIF score of 0.4802 and an SSIM score of 0.9622, improving over the state-of-the-art Deep3D method by 6.69% and 0.86%, respectively. On the widely used LS3D-300W dataset, we obtain a VIF score of 0.3922 and an SSIM score of 0.9079 for indoor images, and the scores for outdoor images are 0.4100 and 0.9160, respectively, which also represent an improvement over those of Deep3D. These results show that our method is able to recover visually more realistic facial appearance details compared with previous methods
Efficient Spatial Reasoning for Human Pose Estimation
No full textHuman pose estimation from single images has made significant progress in the past but still faces fundamental challenges from the occlusion and overlapping of joints in many cases. This is partly due to the limitation of the traditional paradigm for this problem, which attempts to locate human body joints solely and as a result can fail to resolve the spatial connections among joints that are critical for the identification of the whole pose. To overcome this shortcoming, we propose to explicitly incorporate spatial reasoning into pose estimation by formulating it as a structured graph learning problem, in which each image pixel is a candidate graph node with every two nodes connected via an edge that captures their affinity. The advantage of this representation is that it allows us to learn feature embeddings for both the nodes and edges, thereby providing a sufficient capacity to delineate correct human body joints and their connecting bones. To facilitate efficient learning and inference, we exploit self-attention transformer architectures that fuse node and edge learning pathways, which can save parameter numbers and permit fast computation. Experiments on the popular MS-COCO Human pose estimation benchmark show that our method outperforms representative methods
DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction
No full textPublisher Copyright: © The Author(s) 2023. Published by Oxford University Press.MOTIVATION: Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep learning-based methods for this problem cannot achieve satisfactory performance, especially for non-9-mer peptides. This is because such methods generate the input by simply concatenating the two given sequences: a peptide and (the pseudo sequence of) an MHC class I molecule, which cannot precisely capture the anchor positions of the MHC binding motif for the peptides with variable lengths. We thus developed an anchor position-aware and high-performance deep model, DeepMHCI, with a position-wise gated layer and a residual binding interaction convolution layer. This allows the model to control the information flow in peptides to be aware of anchor positions and model the interactions between peptides and the MHC pseudo (binding) sequence directly with multiple convolutional kernels. RESULTS: The performance of DeepMHCI has been thoroughly validated by extensive experiments on four benchmark datasets under various settings, such as 5-fold cross-validation, validation with the independent testing set, external HPV vaccine identification, and external CD8+ epitope identification. Experimental results with visualization of binding motifs demonstrate that DeepMHCI outperformed all competing methods, especially on non-9-mer peptides binding prediction. AVAILABILITY AND IMPLEMENTATION: DeepMHCI is publicly available at https://github.com/ZhuLab-Fudan/DeepMHCI.Peer reviewe
Challenges of achieving digital transformation in manufacturing firms: the case of predictive maintenance and spare part inventory management Journal of Manufacturing Technology Management
No full textPurpose Predictive maintenance (PdM) has attracted increasing attention in recent years owing to the emergence of advanced condition-monitoring technologies and data analytics tools. However, the application of PdM in spare parts inventory management across the Supply Chain (SC) has not been sufficiently investigated and its Digital Transformation (DT) requirements have not been adequately researched. Therefore, this study aims to analyse the organisational readiness for the use of integrated spare parts inventory management together with PdM systems across the SC. Design/methodology/approach A series of semi-structured interviews were designed and took place across organisations in various industries to address the pre-defined research aim. In total, 15 interviewees were recruited through purposive sampling, including managers and technicians in various organisations from different industries. Findings The findings reveal that while maintenance planning and optimisation has been the subject of extensive research for decades, manufacturers are still encountering barriers in adopting and implementing digital innovations. The experts also highlighted the need for an integrated Information System (IS) enabling data sharing across the organisation since lack of integration has a vital impact on the overall business and operations performance as well as the successful DT of the enterprise. In addition, they report that the necessary and relevant data for implementing PdM is not captured or stored in their organisations. Originality The present study emphasises the technical, organisational, and environmental (TOE) dimensions that can affect such DT, and sheds light on the enablers and inhibitors that organisations face in their efforts to be technologically ready to embrace the digital integration of PdM with spare part inventory management. It is recommended that a clear shift in management mindset and organisational culture is necessary for companies to realise the benefits of PdM and the DT that will result from its implementation
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