42-Issue 6

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https://diglib.eg.org/handle/10.2312/3543887

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OaIF: Occlusion‐Aware Implicit Function for Clothed Human Re‐construction
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Tan, Yudi; Guan, Boliang; Zhou, Fan; Su, Zhuo; Hauser, Helwig and Alliez, Pierre
Clothed human re‐construction from a monocular image is challenging due to occlusion, depth‐ambiguity and variations of body poses. Recently, shape representation based on an implicit function, compared to explicit representation such as mesh and voxel, is more capable with complex topology of clothed human. This is mainly achieved by using pixel‐aligned features, facilitating implicit function to capture local details. But such methods utilize an identical feature map for all sampled points to get local features, making their models occlusion‐agnostic in the encoding stage. The decoder, as implicit function, only maps features and does not take occlusion into account explicitly. Thus, these methods fail to generalize well in poses with severe self‐occlusion. To address this, we present OaIF to encode local features conditioned in visibility of SMPL vertices. OaIF projects SMPL vertices onto image plane to obtain image features masked by visibility. Vertices features integrated with geometry information of mesh are then feed into a GAT network to encode jointly. We query hybrid features and occlusion factors for points through cross attention and learn occupancy fields for clothed human. The experiments demonstrate that OaIF achieves more robust and accurate re‐construction than the state of the art on both public datasets and wild images.
Triangle Influence Supersets for Fast Distance Computation
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Pujol, Eduard; Chica, Antonio; Hauser, Helwig and Alliez, Pierre
We present an acceleration structure to efficiently query the Signed Distance Field (SDF) of volumes represented by triangle meshes. The method is based on a discretization of space. In each node, we store the triangles defining the SDF behaviour in that region. Consequently, we reduce the cost of the nearest triangle search, prioritizing query performance, while avoiding approximations of the field. We propose a method to conservatively compute the set of triangles influencing each node. Given a node, each triangle defines a region of space such that all points inside it are closer to a point in the node than the triangle is. This property is used to build the SDF acceleration structure. We do not need to explicitly compute these regions, which is crucial to the performance of our approach. We prove the correctness of the proposed method and compare it to similar approaches, confirming that our method produces faster query times than other exact methods.
A Survey of Personalized Interior Design
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Wang, Y.T.; Liang, C.; Huai, N.; Chen, J.; Zhang, C.J.; Hauser, Helwig and Alliez, Pierre
Interior design is the core step of interior decoration, and it determines the overall layout and style of furniture. Traditional interior design is usually laborious and time‐consuming work carried out by professional designers and cannot always meet clients' personalized requirements. With the development of computer graphics, computer vision and machine learning, computer scientists have carried out much fruitful research work in computer‐aided personalized interior design (PID). In general, personalization research in interior design mainly focuses on furniture selection and floor plan preparation. In terms of the former, personalized furniture selection is achieved by selecting furniture that matches the resident's preference and style, while the latter allows the resident to personalize their floor plan design and planning. Finally, the automatic furniture layout task generates a stylistically matched and functionally complete furniture layout result based on the selected furniture and prepared floor plan. Therefore, the main challenge for PID is meeting residents' personalized requirements in terms of both furniture and floor plans. This paper answers the above question by reviewing recent progress in five separate but correlated areas, including furniture style analysis, furniture compatibility prediction, floor plan design, floor plan analysis and automatic furniture layout. For each topic, we review representative methods and compare and discuss their strengths and shortcomings. In addition, we collect and summarize public datasets related to PID and finally discuss its future research directions.
Reference‐based Screentone Transfer via Pattern Correspondence and Regularization
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Li, Zhansheng; Zhao, Nanxuan; Wu, Zongwei; Dai, Yihua; Wang, Junle; Jing, Yanqing; He, Shengfeng; Hauser, Helwig and Alliez, Pierre
Adding screentone to initial line drawings is a crucial step for manga generation, but is a tedious and human‐laborious task. In this work, we propose a novel data‐driven method aiming to transfer the screentone pattern from a reference manga image. This not only ensures the quality, but also adds controllability to the generated manga results. The reference‐based screentone translation task imposes several unique challenges. Since manga image often contains multiple screentone patterns interweaved with line drawing, as an abstract art, this makes it even more difficult to extract disentangled style code from the reference. Also, finding correspondence for mapping between the reference and the input line drawing without any screentone is hard. As screentone contains many subtle details, how to guarantee the style consistency to the reference remains challenging. To suit our purpose and resolve the above difficulties, we propose a novel Reference‐based Screentone Transfer Network (RSTN). We encode the screentone style through a 1D stylegram. A patch correspondence loss is designed to build a similarity mapping function for guiding the translation. To mitigate the generated artefacts, a pattern regularization loss is introduced in the patch‐level. Through extensive experiments and a user study, we have demonstrated the effectiveness of our proposed model.
A Characterization of Interactive Visual Data Stories With a Spatio‐Temporal Context
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Mayer, Benedikt; Steinhauer, Nastasja; Preim, Bernhard; Meuschke, Monique; Hauser, Helwig and Alliez, Pierre
Large‐scale issues with a spatial and temporal context such as the COVID‐19 pandemic, the war against Ukraine, and climate change have given visual storytelling with data a lot of attention in online journalism, confirming its high effectiveness and relevance for conveying stories. Thus, new ways have emerged that expand the space of visual storytelling techniques. However, interactive visual data stories with a spatio‐temporal context have not been extensively studied yet. Particularly quantitative information about the used layout and media, the visual storytelling techniques, and the visual encoding of space‐time is relevant to get a deeper understanding of how such stories are commonly built to convey complex information in a comprehensible way. Covering these three aspects, we propose a design space derived by merging and adjusting existing approaches, which we used to categorize 130 collected web‐based visual data stories with a spatio‐temporal context from between 2018 and 2022. An analyzis of the collected data reveals the power of large‐scale issues to shape the landscape of storytelling techniques and a trend towards a simplified consumability of stories. Taken together, our findings can serve story authors as inspiration regarding which storytelling techniques to include in their own spatio‐temporal data stories.
tachyon: Efficient Shared Memory Parallel Computation of Extremum Graphs
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Ande, Abhijath; Subhash, Varshini; Natarajan, Vijay; Hauser, Helwig and Alliez, Pierre
The extremum graph is a succinct representation of the Morse decomposition of a scalar field. It has increasingly become a useful data structure that supports topological feature‐directed visualization of 2D/3D scalar fields, and enables dimensionality reduction together with exploratory analysis of high‐dimensional scalar fields. Current methods that employ the extremum graph compute it either using a simple sequential algorithm for computing the Morse decomposition or by computing the more detailed Morse–Smale complex. Both approaches are typically limited to two and three‐dimensional scalar fields. We describe a GPU–CPU hybrid parallel algorithm for computing the extremum graph of scalar fields in all dimensions. The proposed shared memory algorithm utilizes both fine‐grained parallelism and task parallelism to achieve efficiency. An open source software library, , that implements the algorithm exhibits superior performance and good scaling behaviour.
Line Drawing Vectorization via Coarse‐to‐Fine Curve Network Optimization
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Bao, Bin; Fu, Hongbo; Hauser, Helwig and Alliez, Pierre
Vectorizing line drawings is a fundamental component of the workflow in various applications such as graphic design and computer animation. A practical vectorization tool is desired to produce high‐quality curves that are faithful to the original inputs and close to the connectivity of human drawings. The existing line vectorization approaches either suffer from low geometry accuracy or incorrect connectivity for noisy inputs or detailed complex drawings. We propose a novel line drawing vectorization framework based on coarse‐to‐fine curve network optimization. Our technique starts with an initial curve network generated by an existing tracing method. It then performs a global optimization which fits the curve network to image centrelines. Finally, our method performs a finer optimization in local junction regions to achieve better connectivity and curve geometry around junctions. We qualitatively and quantitatively evaluate our system on line drawings with varying image quality and shape complexity, and show that our technique outperforms existing works in terms of curve quality and computational time.
Break and Splice: A Statistical Method for Non‐Rigid Point Cloud Registration
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Gao, Qinghong; Zhao, Yan; Xi, Long; Tang, Wen; Wan, Tao Ruan; Hauser, Helwig and Alliez, Pierre
3D object matching and registration on point clouds are widely used in computer vision. However, most existing point cloud registration methods have limitations in handling non‐rigid point sets or topology changes (. connections and separations). As a result, critical characteristics such as large inter‐frame motions of the point clouds may not be accurately captured. This paper proposes a statistical algorithm for non‐rigid point sets registration, addressing the challenge of handling topology changes without the need to estimate correspondence. The algorithm uses a novel framework to treat the non‐rigid registration challenges as a reproduction process and a Dirichlet Process Gaussian Mixture Model (DPGMM) to cluster a pair of point sets. Labels are assigned to the source point set with an iterative classification procedure, and the source is registered to the target with the same labels using the Bayesian Coherent Point Drift (BCPD) method. The results demonstrate that the proposed approach achieves lower registration errors and efficiently registers point sets undergoing topology changes and large inter‐frame motions. The proposed approach is evaluated on several data sets using various qualitative and quantitative metrics. The results demonstrate that the framework outperforms state‐of‐the‐art methods, achieving an average error reduction of about 60% and a registration time reduction of about 57.8%.
Feature Representation for High‐resolution Clothed Human Reconstruction
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Pu, Juncheng; Liu, Li; Fu, Xiaodong; Su, Zhuo; Liu, Lijun; Peng, Wei; Hauser, Helwig and Alliez, Pierre
Detailed and accurate feature representation is essential for high‐resolution reconstruction of clothed human. Herein we introduce a unified feature representation for clothed human reconstruction, which can adapt to changeable posture and various clothing details. The whole method can be divided into two parts: the human shape feature representation and the details feature representation. Specifically, we firstly combine the voxel feature learned from semantic voxel with the pixel feature from input image as an implicit representation for human shape. Then, the details feature mixed with the clothed layer feature and the normal feature is used to guide the multi‐layer perceptron to capture geometric surface details. The key difference from existing methods is that we use the clothing semantics to infer clothed layer information, and further restore the layer details with geometric height. We qualitative and quantitative experience results demonstrate that proposed method outperforms existing methods in terms of handling limb swing and clothing details. Our method provides a new solution for clothed human reconstruction with high‐resolution details (style, wrinkles and clothed layers), and has good potential in three‐dimensional virtual try‐on and digital characters.
Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Li, Zhiqi; Xiang, Nan; Chen, Honghua; Zhang, Jianjun; Yang, Xiaosong; Hauser, Helwig and Alliez, Pierre
Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow estimation has been an interest of research in computer vision and computer graphics for a long time. It is also a fundamental task for various applications such as autonomous driving. Compared to previous methods that utilize image representations, many recent researches build upon the power of deep analysis and focus on point clouds representation to conduct 3D flow estimation. This paper comprehensively reviews the pioneering literature in scene flow estimation based on point clouds. Meanwhile, it delves into detail in learning paradigms and presents insightful comparisons between the state‐of‐the‐art methods using deep learning for scene flow estimation. Furthermore, this paper investigates various higher‐level scene understanding tasks, including object tracking, motion segmentation, etc. and concludes with an overview of foreseeable research trends for scene flow estimation.
ROI Scissor: Interactive Segmentation of Feature Region of Interest in a Triangular Mesh
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Moon, Ji‐Hye; Ha, Yujin; Park, Sanghun; Kim, Myung‐Soo; Yoon, Seung‐Hyun; Hauser, Helwig and Alliez, Pierre
We present a simple and effective method for the interactive segmentation of feature regions in a triangular mesh. From the user‐specified radius and click position, the candidate region that contains the desired feature region is defined as geodesic disc on a triangle mesh. A concavity‐aware harmonic field is then computed on the candidate region using the appropriate boundary constraints. An initial isoline is chosen by evaluating the uniformly sampled ones on the harmonic field based on the gradient magnitude. A set of feature points on the initial isoline is selected and the anisotropic geodesics passing through them are then determined as the final segmentation boundary, which is smooth and locally shortest. The experimental results show several segmentation results for various 3D models, revealing the effectiveness of the proposed method.
Texture Inpainting for Photogrammetric Models
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Maggiordomo, A.; Cignoni, P.; Tarini, M.; Hauser, Helwig and Alliez, Pierre
We devise a technique designed to remove the texturing artefacts that are typical of 3D models representing real‐world objects, acquired by photogrammetric techniques. Our technique leverages the recent advancements in inpainting of natural colour images, adapting them to the specific context. A neural network, modified and trained for our purposes, replaces the texture areas containing the defects, substituting them with new plausible patches of texels, reconstructed from the surrounding surface texture. We train and apply the network model on locally reparametrized texture patches, so to provide an input that simplifies the learning process, because it avoids any texture seams, unused texture areas, background, depth jumps and so on. We automatically extract appropriate training data from real‐world datasets. We show two applications of the resulting method: one, as a fully automatic tool, addressing all problems that can be detected by analysing the UV‐map of the input model; and another, as an interactive semi‐automatic tool, presented to the user as a 3D ‘fixing’ brush that has the effect of removing artefacts from any zone the users paints on. We demonstrate our method on a variety of real‐world inputs and provide a reference usable implementation.
Model‐based Crowd Behaviours in Human‐solution Space
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Xiang, Wei; Wang, He; Zhang, Yuqing; Yip, Milo K.; Jin, Xiaogang; Hauser, Helwig and Alliez, Pierre
Realistic crowd simulation has been pursued for decades, but it still necessitates tedious human labour and a lot of trial and error. The majority of currently used crowd modelling is either empirical (model‐based) or data‐driven (model‐free). Model‐based methods cannot fit observed data precisely, whereas model‐free methods are limited by the availability/quality of data and are uninterpretable. In this paper, we aim at taking advantage of both model‐based and data‐driven approaches. In order to accomplish this, we propose a new simulation framework built on a physics‐based model that is designed to be data‐friendly. Both the general prior knowledge about crowds encoded by the physics‐based model and the specific real‐world crowd data at hand jointly influence the system dynamics. With a multi‐granularity physics‐based model, the framework combines microscopic and macroscopic motion control. Each simulation step is formulated as an energy optimization problem, where the minimizer is the desired crowd behaviour. In contrast to traditional optimization‐based methods which seek the theoretical minimizer, we designed an acceleration‐aware data‐driven scheme to compute the minimizer from real‐world data in order to achieve higher realism by parameterizing both velocity and acceleration. Experiments demonstrate that our method can produce crowd animations that are more realistically behaved in a variety of scales and scenarios when compared to the earlier methods.
EvIcon: Designing High‐Usability Icon with Human‐in‐the‐loop Exploration and IconCLIP
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Shen, I‐Chao; Cherng, Fu‐Yin; Igarashi, Takeo; Lin, Wen‐Chieh; Chen, Bing‐Yu; Hauser, Helwig and Alliez, Pierre
Interface icons are prevalent in various digital applications. Due to limited time and budgets, many designers rely on informal evaluation, which often results in poor usability icons. In this paper, we propose a unique human‐in‐the‐loop framework that allows our target users, that is novice and professional user interface (UI) designers, to improve the usability of interface icons efficiently. We formulate several usability criteria into a perceptual usability function and enable users to iteratively revise an icon set with an interactive design tool, EvIcon. We take a large‐scale pre‐trained joint image‐text embedding (CLIP) and fine‐tune it to embed icon visuals with icon tags in the same embedding space (IconCLIP). During the revision process, our design tool provides two types of instant perceptual usability feedback. First, we provide perceptual usability feedback modelled by deep learning models trained on IconCLIP embeddings and crowdsourced perceptual ratings. Second, we use the embedding space of IconCLIP to assist users in improving icons' visual distinguishability among icons within the user‐prepared icon set. To provide the perceptual prediction, we compiled , the first large‐scale dataset of perceptual usability ratings over 10,000 interface icons, by conducting a crowdsourcing study. We demonstrated that our framework could benefit UI designers' interface icon revision process with a wide range of professional experience. Moreover, the interface icons designed using our framework achieved better semantic distance and familiarity, verified by an additional online user study.
Distributed Poisson Surface Reconstruction
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Kazhdan, M.; Hoppe, H.; Hauser, Helwig and Alliez, Pierre
Screened Poisson surface reconstruction robustly creates meshes from oriented point sets. For large datasets, the technique requires hours of computation and significant memory. We present a method to parallelize and distribute this computation over multiple commodity client nodes. The method partitions space on one axis into adaptively sized slabs containing balanced subsets of points. Because the Poisson formulation involves a global system, the challenge is to maintain seamless consistency at the slab boundaries and obtain a reconstruction that is indistinguishable from the serial result. To this end, we express the reconstructed indicator function as a sum of a low‐resolution term computed on a server and high‐resolution terms computed on distributed clients. Using a client–server architecture, we map the computation onto a sequence of serial server tasks and parallel client tasks, separated by synchronization barriers. This architecture also enables low‐memory evaluation on a single computer, albeit without speedup. We demonstrate a 700 million vertex reconstruction of the billion point David statue scan in less than 20 min on a 65‐node cluster with a maximum memory usage of 45 GB/node, or in 14 h on a single node.
Visually Abstracting Event Sequences as Double Trees Enriched with Category‐Based Comparison
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Krause, Cedric; Agarwal, Shivam; Burch, Michael; Beck, Fabian; Hauser, Helwig and Alliez, Pierre
Event sequence visualization aids analysts in many domains to better understand and infer new insights from event data. Analysing behaviour before or after a certain event of interest is a common task in many scenarios. In this paper, we introduce, formally define, and position as a domain‐agnostic tree visualization approach for this task. The visualization shows the sequences that led to the event of interest as a tree on the left, and those that followed on the right. Moreover, our approach enables users to create selections based on event attributes to interactively compare the events and sequences along colour‐coded categories. We integrate the double tree and category‐based comparison into a user interface for event sequence analysis. In three application examples, we show a diverse set of scenarios, covering short and long time spans, non‐spatial and spatial events, human and artificial actors, to demonstrate the general applicability of the approach.
3D Generative Model Latent Disentanglement via Local Eigenprojection
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Foti, Simone; Koo, Bongjin; Stoyanov, Danail; Clarkson, Matthew J.; Hauser, Helwig and Alliez, Pierre
Designing realistic digital humans is extremely complex. Most data‐driven generative models used to simplify the creation of their underlying geometric shape do not offer control over the generation of local shape attributes. In this paper, we overcome this limitation by introducing a novel loss function grounded in spectral geometry and applicable to different neural‐network‐based generative models of 3D head and body meshes. Encouraging the latent variables of mesh variational autoencoders (VAEs) or generative adversarial networks (GANs) to follow the local eigenprojections of identity attributes, we improve latent disentanglement and properly decouple the attribute creation. Experimental results show that our local eigenprojection disentangled (LED) models not only offer improved disentanglement with respect to the state‐of‐the‐art, but also maintain good generation capabilities with training times comparable to the vanilla implementations of the models. Our code and pre‐trained models are available at .
MesoGAN: Generative Neural Reflectance Shells
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Diolatzis, Stavros; Novak, Jan; Rousselle, Fabrice; Granskog, Jonathan; Aittala, Miika; Ramamoorthi, Ravi; Drettakis, George; Hauser, Helwig and Alliez, Pierre
We introduce MesoGAN, a model for generative 3D neural textures. This new graphics primitive represents mesoscale appearance by combining the strengths of generative adversarial networks (StyleGAN) and volumetric neural field rendering. The primitive can be applied to surfaces as a neural reflectance shell; a thin volumetric layer above the surface with appearance parameters defined by a neural network. To construct the neural shell, we first generate a 2D feature texture using StyleGAN with carefully randomized Fourier features to support arbitrarily sized textures without repeating artefacts. We augment the 2D feature texture with a learned height feature, which aids the neural field renderer in producing volumetric parameters from the 2D texture. To facilitate filtering, and to enable end‐to‐end training within memory constraints of current hardware, we utilize a hierarchical texturing approach and train our model on multi‐scale synthetic datasets of 3D mesoscale structures. We propose one possible approach for conditioning MesoGAN on artistic parameters (e.g. fibre length, density of strands, lighting direction) and demonstrate and discuss integration into physically based renderers.
Numerical Coarsening with Neural Shape Functions
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Ni, Ning; Xu, Qingyu; Li, Zhehao; Fu, Xiao‐Ming; Liu, Ligang; Hauser, Helwig and Alliez, Pierre
We propose to use nonlinear shape functions represented as neural networks in numerical coarsening to achieve generalization capability as well as good accuracy. To overcome the challenge of generalization to different simulation scenarios, especially nonlinear materials under large deformations, our key idea is to replace the linear mapping between coarse and fine meshes adopted in previous works with a nonlinear one represented by neural networks. However, directly applying an end‐to‐end neural representation leads to poor performance due to over‐huge parameter space as well as failing to capture some intrinsic geometry properties of shape functions. Our solution is to embed geometry constraints as the prior knowledge in learning, which greatly improves training efficiency and inference robustness. With the trained neural shape functions, we can easily adopt numerical coarsening in the simulation of various hyperelastic models without any other preprocessing step required. The experiment results demonstrate the efficiency and generalization capability of our method over previous works.
A Semi‐Procedural Convolutional Material Prior
(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Zhou, Xilong; Hašan, Miloš; Deschaintre, Valentin; Guerrero, Paul; Sunkavalli, Kalyan; Kalantari, Nima Khademi; Hauser, Helwig and Alliez, Pierre
Lightweight material capture methods require a material prior, defining the subspace of plausible textures within the large space of unconstrained texel grids. Previous work has either used deep neural networks (trained on large synthetic material datasets) or procedural node graphs (constructed by expert artists) as such priors. In this paper, we propose a semi‐procedural differentiable material prior that represents materials as a set of (typically procedural) grayscale noises and patterns that are processed by a sequence of lightweight learnable convolutional filter operations. We demonstrate that the restricted structure of this architecture acts as an inductive bias on the space of material appearances, allowing us to optimize the weights of the convolutions per‐material, with no need for pre‐training on a large dataset. Combined with a differentiable rendering step and a perceptual loss, we enable single‐image tileable material capture comparable with state of the art. Our approach does not target the pixel‐perfect recovery of the material, but rather uses noises and patterns as input to match the target appearance. To achieve this, it does not require complex procedural graphs, and has a much lower complexity, computational cost and storage cost. We also enable control over the results, through changing the provided patterns and using guide maps to push the material properties towards a user‐driven objective.

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