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MOTIV: Visual Exploration of Moral Framing in Social Media Comput. Graph. Forum (IF 2.5) Pub Date : 2024-03-28 A. Wentzel, L. Levine, V. Dhariwal, Z. Fatemi, A. Bhattacharya, B. Di Eugenio, A. Rojecki, E. Zheleva, G.E. Marai
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Deep and Fast Approximate Order Independent Transparency Comput. Graph. Forum (IF 2.5) Pub Date : 2024-03-06 Grigoris Tsopouridis, Andreas A. Vasilakis, Ioannis Fudos
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DeforestVis: Behaviour Analysis of Machine Learning Models with Surrogate Decision Stumps Comput. Graph. Forum (IF 2.5) Pub Date : 2024-02-28 Angelos Chatzimparmpas, Rafeal M. Martins, Alexandru C. Telea, Andreas Kerren
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Real‐Time Polygonal Lighting of Iridescence Effect using Precomputed Monomial‐Gaussians Comput. Graph. Forum (IF 2.5) Pub Date : 2024-02-20 Zhengze Liu, Yuchi Huo, Yinhui Yang, Jie Chen, Rui Wang
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Guided Exploration of Industrial Sensor Data Comput. Graph. Forum (IF 2.5) Pub Date : 2024-01-29 Tristan Langer, Richard Meyes, Tobias Meisen
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End-to-End Compressed Meshlet Rendering Comput. Graph. Forum (IF 2.5) Pub Date : 2024-01-24 D. Mlakar, M. Steinberger, D. Schmalstieg
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PPSurf: Combining Patches and Point Convolutions for Detailed Surface Reconstruction Comput. Graph. Forum (IF 2.5) Pub Date : 2024-01-12 Philipp Erler, Lizeth Fuentes-Perez, Pedro Hermosilla, Paul Guerrero, Renato Pajarola, Michael Wimmer
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Visualization of Escher-like Kaleidoscopic Spherical Patterns of Regular Polyhedron Symmetry Comput. Graph. Forum (IF 2.5) Pub Date : 2024-01-03 Krzysztof Gdawiec, Kwok Wai Chung, Alain Nicolas, David Bailey, Peichang Ouyang
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State of the Art in Efficient Translucent Material Rendering with BSSRDF Comput. Graph. Forum (IF 2.5) Pub Date : 2023-12-22 Shiyu Liang, Yang Gao, Chonghao Hu, Peng Zhou, Aimin Hao, Lili Wang, Hong Qin
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Neural Path Sampling for Rendering Pure Specular Light Transport Comput. Graph. Forum (IF 2.5) Pub Date : 2023-12-19 Rui Yu, Yue Dong, Youkang Kong, Xin Tong
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Simplified Physical Model-based Balance-preserving Motion Re-targeting for Physical Simulation Comput. Graph. Forum (IF 2.5) Pub Date : 2023-12-11 Jaepyung Hwang, Shin Ishii
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Front Matter Comput. Graph. Forum (IF 2.5) Pub Date : 2023-12-06
High-Performance Graphics 2023 Delft, The Netherlands June 26 — 28, 2023 General Chairs Quirin Meyer, Hochschule Coburg David McAllister, AMD Papers Chairs Jacco Bikker, Traverse Research Christiaan Gribble, AMD
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Formation-Aware Planning and Navigation with Corridor Shortest Path Maps Comput. Graph. Forum (IF 2.5) Pub Date : 2023-12-04 Ritesh Sharma, Tomer Weiss, Marcelo Kallmann
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Identifying and Visualizing Terrestrial Magnetospheric Topology using Geodesic Level Set Method Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-27 Peikun Xiong, Shigeru Fujita, Masakazu Watanabe, Takashi Tanaka, Dongsheng Cai
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Real-time Terrain Enhancement with Controlled Procedural Patterns Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-23 C. Grenier, É. Guérin, É. Galin, B. Sauvage
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Curvature-driven Multi-stream Network for Feature-preserving Mesh Denoising Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-23 Zhibo Zhao, Wenming Tang, Yuanhao Gong
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SkyGAN: Realistic Cloud Imagery for Image-based Lighting Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-17 Martin Mirbauer, Tobias Rittig, Tomáš Iser, Jaroslav Křivánek, Elena Šikudová
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Groupwise Shape Correspondence Refinement with a Region of Interest Focus Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-14 Pierre Galmiche, Hyewon Seo
While collections of scan shapes are becoming more prevalent in many real-world applications, finding accurate and dense correspondences across multiple shapes remains a challenging task. In this work, we introduce a new approach for refining non-rigid correspondences among a collection of 3D shapes undergoing non-rigid deformation. Our approach incorporates a Region Of Interest (ROI) into the refinement
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Balancing Rotation Minimizing Frames with Additional Objectives Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-14 C. Mossman, R. H. Bartels, F. F. Samavati
When moving along 3D curves, one may require local coordinate frames for visited points, such as for animating virtual cameras, controlling robotic motion, or constructing sweep surfaces. Often, consecutive coordinate frames should be similar, avoiding sharp twists. Previous work achieved this goal by using various methods to approximate rotation minimizing frames (RMFs) with respect to a curve's tangent
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Neural Impostor: Editing Neural Radiance Fields with Explicit Shape Manipulation Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-14 Ruiyang Liu, Jinxu Xiang, Bowen Zhao, Ran Zhang, Jingyi Yu, Changxi Zheng
Neural Radiance Fields (NeRF) have significantly advanced the generation of highly realistic and expressive 3D scenes. However, the task of editing NeRF, particularly in terms of geometry modification, poses a significant challenge. This issue has obstructed NeRF's wider adoption across various applications. To tackle the problem of efficiently editing neural implicit fields, we introduce Neural Impostor
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SVBRDF Reconstruction by Transferring Lighting Knowledge Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Pengfei Zhu, Shuichang Lai, Mufan Chen, Jie Guo, Yifan Liu, Yanwen Guo
The problem of reconstructing spatially-varying BRDFs from RGB images has been studied for decades. Researchers found themselves in a dilemma: opting for either higher quality with the inconvenience of camera and light calibration, or greater convenience at the expense of compromised quality without complex setups. We address this challenge by introducing a two-branch network to learn the lighting
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Fine Back Surfaces Oriented Human Reconstruction for Single RGB-D Images Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Xianyong Fang, Yu Qian, Jinshen He, Linbo Wang, Zhengyi Liu
Current single RGB-D image based human surface reconstruction methods generally take both the RGB images and the captured frontal depth maps together so that the 3D cues from the frontal surfaces can help infer the full surface geometries. However, we observe that the back surfaces can often be quite different from the frontal surfaces and, therefore, current methods can mess the recovery process by
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World-Space Spatiotemporal Path Resampling for Path Tracing Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Hangyu Zhang, Beibei Wang
With the advent of hardware-accelerated ray tracing, more and more real-time rendering applications tend to render images with ray-traced global illumination (GI). However, the low sample counts at real-time framerates bring enormous challenges to existing path sampling methods. Recent work (ReSTIR GI) samples indirect illumination effectively with a dramatic bias reduction. However, as a screen-space
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Authoring Terrains with Spatialised Style Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Simon Perche, Adrien Peytavie, Bedrich Benes, Eric Galin, Eric Guérin
Various terrain modelling methods have been proposed for the past decades, providing efficient and often interactive authoring tools. However, they seldom include any notion of style, which is critical for designers in the entertainment industry. We introduce a new generative network method that bridges the gap between automatic terrain synthesis and authoring, providing a versatile set of authoring
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Data-guided Authoring of Procedural Models of Shapes Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Ishtiaque Hossain, I-Chao Shen, Takeo Igarashi, Oliver van Kaick
Procedural models enable the generation of a large amount of diverse shapes by varying the parameters of the model. However, writing a procedural model for replicating a collection of reference shapes is difficult, requiring much inspection of the original and replicated shapes during the development of the model. In this paper, we introduce a data-guided method for aiding a programmer in creating
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Interactive Authoring of Terrain using Diffusion Models Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 J. Lochner, J. Gain, S. Perche, A. Peytavie, E. Galin, E. Guérin
Generating heightfield terrains is a necessary precursor to the depiction of computer-generated natural scenes in a variety of applications. Authoring such terrains is made challenging by the need for interactive feedback, effective user control, and perceptually realistic output encompassing a range of landforms. We address these challenges by developing a terrain-authoring framework underpinned by
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DAFNet: Generating Diverse Actions for Furniture Interaction by Learning Conditional Pose Distribution Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Taeil Jin, Sung-Hee Lee
We present DAFNet, a novel data-driven framework capable of generating various actions for indoor environment interactions. By taking desired root and upper-body poses as control inputs, DAFNet generates whole-body poses suitable for furniture of various shapes and combinations. To enable the generation of diverse actions, we introduce an action predictor that automatically infers the probabilities
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Efficient Interpolation of Rough Line Drawings Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 J. Chen, X. Zhu, M. Even, J. Basset, P. Bénard, P. Barla
In traditional 2D animation, sketches drawn at distant keyframes are used to design motion, yet it would be far too labor-intensive to draw all the inbetween frames to fully visualize that motion. We propose a novel efficient interpolation algorithm that generates these intermediate frames in the artist's drawing style. Starting from a set of registered rough vector drawings, we first generate a large
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Refinement of Hair Geometry by Strand Integration Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Ryota Maeda, Kenshi Takayama, Takafumi Taketomi
Reconstructing 3D hair is challenging due to its complex micro-scale geometry, and is of essential importance for the efficient creation of high-fidelity virtual humans. Existing hair capture methods based on multi-view stereo tend to generate results that are noisy and inaccurate. In this study, we propose a refinement method for hair geometry by incorporating the gradient of strands into the computation
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OptCtrlPoints: Finding the Optimal Control Points for Biharmonic 3D Shape Deformation Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Kunho Kim, Mikaela Angelina Uy, Despoina Paschalidou, Alec Jacobson, Leonidas J. Guibas, Minhyuk Sung
We propose OptCtrlPoints, a data-driven framework designed to identify the optimal sparse set of control points for reproducing target shapes using biharmonic 3D shape deformation. Control-point-based 3D deformation methods are widely utilized for interactive shape editing, and their usability is enhanced when the control points are sparse yet strategically distributed across the shape. With this objective
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Controllable Garment Image Synthesis Integrated with Frequency Domain Features Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Xinru Liang, Haoran Mo, Chengying Gao
Using sketches and textures to synthesize garment images is able to conveniently display the realistic visual effect in the design phase, which greatly increases the efficiency of fashion design. Existing garment image synthesis methods from a sketch and a texture tend to fail in working on complex textures, especially those with periodic patterns. We propose a controllable garment image synthesis
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D-Cloth: Skinning-based Cloth Dynamic Prediction with a Three-stage Network Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Y. D. Li, M. Tang, X. R. Chen, Y. Yang, R. F. Tong, B. L. An, S. C. Yang, Y. Li, Q. L. Kou
We propose a three-stage network that utilizes a skinning-based model to accurately predict dynamic cloth deformation. Our approach decomposes cloth deformation into three distinct components: static, coarse dynamic, and wrinkle dynamic components. To capture these components, we train our three-stage network accordingly. In the first stage, the static component is predicted by constructing a static
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MOVIN: Real-time Motion Capture using a Single LiDAR Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Deok-Kyeong Jang, Dongseok Yang, Deok-Yun Jang, Byeoli Choi, Taeil Jin, Sung-Hee Lee
Recent advancements in technology have brought forth new forms of interactive applications, such as the social metaverse, where end users interact with each other through their virtual avatars. In such applications, precise full-body tracking is essential for an immersive experience and a sense of embodiment with the virtual avatar. However, current motion capture systems are not easily accessible
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Precomputed Radiative Heat Transport for Efficient Thermal Simulation Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 C. Freude, D. Hahn, F. Rist, L. Lipp, M. Wimmer
Architectural design and urban planning are complex design tasks. Predicting the thermal impact of design choices at interactive rates enhances the ability of designers to improve energy efficiency and avoid problematic heat islands while maintaining design quality. We show how to use and adapt methods from computer graphics to efficiently simulate heat transfer via thermal radiation, thereby improving
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Enhancing Low-Light Images: A Variation-based Retinex with Modified Bilateral Total Variation and Tensor Sparse Coding Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Weipeng Yang, Hongxia Gao, Wenbin Zou, Shasha Huang, Hongsheng Chen, Jianliang Ma
Low-light conditions often result in the presence of significant noise and artifacts in captured images, which can be further exacerbated during the image enhancement process, leading to a decrease in visual quality. This paper aims to present an effective low-light image enhancement model based on the variation Retinex model that successfully suppresses noise and artifacts while preserving image details
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Continuous Layout Editing of Single Images with Diffusion Models Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 Zhiyuan Zhang, Zhitong Huang, Jing Liao
Recent advancements in large-scale text-to-image diffusion models have enabled many applications in image editing. However, none of these methods have been able to edit the layout of single existing images. To address this gap, we propose the first framework for layout editing of a single image while preserving its visual properties, thus allowing for continuous editing on a single image. Our approach
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Multi-scale Iterative Model-guided Unfolding Network for NLOS Reconstruction Comput. Graph. Forum (IF 2.5) Pub Date : 2023-11-05 X. Su, Y. Hong, J. Ye, F. Xu, X. Yuan
Non-line-of-sight (NLOS) imaging can reconstruct hidden objects by analyzing diffuse reflection of relay surfaces, and is potentially used in autonomous driving, medical imaging and national defense. Despite the challenges of low signal-to-noise ratio (SNR) and ill-conditioned problem, NLOS imaging has developed rapidly in recent years. While deep neural networks have achieved impressive success in
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Data-Driven Ink Painting Brushstroke Rendering Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Koki Madono, Edgar Simo-Serra
Although digital painting has advanced much in recent years, there is still a significant divide between physically drawn paintings and purely digitally drawn paintings. These differences arise due to the physical interactions between the brush, ink, and paper, which are hard to emulate in the digital domain. Most ink painting approaches have focused on either using heuristics or physical simulation
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Meso-Skeleton Guided Hexahedral Mesh Design Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 P. Viville, P. Kraemer, D. Bechmann
We present a novel approach for the generation of hexahedral meshes in a volume domain given its meso-skeleton. This compact representation of the topology and geometry, composed of both curve and surface parts, is used to produce a raw decomposition of the domain into hexahedral blocks. Analysis of the different local configurations of the skeleton leads to the construction of a set of connection
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Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-31 D. Xue, J. Vazquez Corral, L. Herranz, Y. Zhang, M. S. Brown
Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations
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A Post Processing Technique to Automatically Remove Floater Artifacts in Neural Radiance Fields Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-31 T. Wirth, A. Rak, V. Knauthe, D. W. Fellner
Neural Radiance Fields have revolutionized Novel View Synthesis by providing impressive levels of realism. However, in most in-the-wild scenes they suffer from floater artifacts that occur due to sparse input images or strong view-dependent effects. We propose an approach that uses neighborhood based clustering and a consistency metric on NeRF models trained on different scene scales to identify regions
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Error-bounded Image Triangulation Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-31 Zhi-Duo Fang, Jia-Peng Guo, Yanyang Xiao, Xiao-Ming Fu
We propose a novel image triangulation method to reduce the complexity of image triangulation under the color error-bounded constraint and the triangle quality constraint. Meanwhile, we realize a variety of visual effects by supporting different types of triangles (e.g., linear or curved) and color approximation functions (e.g., constant, linear, or quadratic). To adapt to these discontinuous and combinatorial
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Efficient Caustics Rendering via Spatial and Temporal Path Reuse Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-31 Xiaofeng Xu, Lu Wang, Beibei Wang
Caustics are complex optical effects caused by the light being concentrated in a small area due to reflection or refraction on surfaces with low roughness, typically under a sharp light source. Rendering caustic effects is challenging for Monte Carlo-based approaches, due to the difficulties of sampling the specular paths. One effective solution is using the specular manifold to locate these valid
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Robust Distribution-Aware Color Correction for Single-Shot Images Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-31 Daljit Singh J. Dhillon, Parisha Joshi, Jessica Baron, Eric K. Patterson
Color correction for photographed images is an ill-posed problem. It is also a crucial initial step towards material acquisition for inverse rendering methods or pipelines. Several state-of-the-art methods rely on reducing color differences for imaged reference color chart blocks of known color values to devise or optimize their solution. In this paper, we first establish through simulations the limitation
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Multi-Modal Face Stylization with a Generative Prior Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Mengtian Li, Yi Dong, Minxuan Lin, Haibin Huang, Pengfei Wan, Chongyang Ma
In this work, we introduce a new approach for face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high-quality artistic faces with diverse styles and accurate facial reconstruction. Our proposed framework, MMFS, supports multi-modal face stylization by leveraging the strengths of StyleGAN and integrates it into an encoder-decoder
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Sharing Model Framework for Zero-Shot Sketch-Based Image Retrieval Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Yi-Hsuan Ho, Der-Lor Way, Zen-Chung Shih
Sketch-based image retrieval (SBIR) is an emerging task in computer vision. Research interests have arisen in solving this problem under the realistic and challenging setting of zero-shot learning. Given a sketch as a query, the search goal is to retrieve the corresponding photographs in a zero-shot scenario. In this paper, we divide the aforementioned challenging work into three tasks and propose
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An Efficient Self-supporting Infill Structure for Computational Fabrication Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Shengfa Wang, Zheng Liu, Jiangbei Hu, Na Lei, Zhongxuan Luo
Efficiently optimizing the internal structure of 3D printing models is a critical focus in the field of industrial manufacturing, particularly when designing self-supporting structures that offer high stiffness and lightweight characteristics. To tackle this challenge, this research introduces a novel approach featuring a self-supporting polyhedral structure and an efficient optimization algorithm
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Reconstructing 3D Human Pose from RGB-D Data with Occlusions Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Bowen Dang, Xi Zhao, Bowen Zhang, He Wang
We propose a new method to reconstruct the 3D human body from RGB-D images with occlusions. The foremost challenge is the incompleteness of the RGB-D data due to occlusions between the body and the environment, leading to implausible reconstructions that suffer from severe human-scene penetration. To reconstruct a semantically and physically plausible human body, we propose to reduce the solution space
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Facial Image Shadow Removal via Graph-based Feature Fusion Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Ling Zhang, Ben Chen, Zheng Liu, Chunxia Xiao
Despite natural image shadow removal methods have made significant progress, they often perform poorly for facial image due to the unique features of the face. Moreover, most learning-based methods are designed based on pixel-level strategies, ignoring the global contextual relationship in the image. In this paper, we propose a graph-based feature fusion network (GraphFFNet) for facial image shadow
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A Surface Subdivision Scheme Based on Four-Directional S13 Non-Box Splines Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Z. Huang
In this paper, we propose a novel surface subdivision scheme called non-box subdivision, which is generalized from four-directional S13 non-box splines. The resulting subdivision surfaces achieve C1 continuity with the convex hull property. This scheme can be regarded as either a four-directional subdivision or a special quadrilateral subdivision. When used as a quadrilateral subdivision, the proposed
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Palette-Based and Harmony-Guided Colorization for Vector Icons Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Miao Lin, I-Chao Shen, Hsiao-Yuan Chin, Ruo-Xi Chen, Bing-Yu Chen
Colorizing icon is a challenging task, even for skillful artists, as it involves balancing aesthetics and practical considerations. Prior works have primarily focused on colorizing pixel-based icons, which do not seamlessly integrate into the current vector-based icon design workflow. In this paper, we propose a palette-based colorization algorithm for vector icons without the need for rasterization
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IBL-NeRF: Image-Based Lighting Formulation of Neural Radiance Fields Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Changwoon Choi, Juhyeon Kim, Young Min Kim
We propose IBL-NeRF, which decomposes the neural radiance fields (NeRF) of large-scale indoor scenes into intrinsic components. Recent approaches further decompose the baked radiance of the implicit volume into intrinsic components such that one can partially approximate the rendering equation. However, they are limited to representing isolated objects with a shared environment lighting, and suffer
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Semantics-guided generative diffusion model with a 3DMM model condition for face swapping Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Xiyao Liu, Yang Liu, Yuhao Zheng, Ting Yang, Jian Zhang, Victoria Wang, Hui Fang
Face swapping is a technique that replaces a face in a target media with another face of a different identity from a source face image. Currently, research on the effective utilisation of prior knowledge and semantic guidance for photo-realistic face swapping remains limited, despite the impressive synthesis quality achieved by recent generative models. In this paper, we propose a novel conditional
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Learning to Generate and Manipulate 3D Radiance Field by a Hierarchical Diffusion Framework with CLIP Latent Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Jiaxu Wang, Ziyi Zhang, Renjing Xu
3D-aware generative adversarial networks (GAN) are widely adopted in generating and editing neural radiance fields (NeRF). However, these methods still suffer from GAN-related issues including degraded diversity and training instability. Moreover, 3D-aware GANs consider NeRF pipeline as regularizers and do not directly operate with 3D assets, leading to imperfect 3D consistencies. Besides, the independent
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Multi-Level Implicit Function for Detailed Human Reconstruction by Relaxing SMPL Constraints Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Xikai Ma, Jieyu Zhao, Yiqing Teng, Li Yao
Aiming at enhancing the rationality and robustness of the results of single-view image-based human reconstruction and acquiring richer surface details, we propose a multi-level reconstruction framework based on implicit functions. This framework first utilizes the predicted SMPL model (Skinned Multi-Person Linear Model) as a prior to further predict consistent 2.5D sketches (depth map and normal map)
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Robust Novel View Synthesis with Color Transform Module Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 S. M. Kim, C. Choi, H. Heo, Y. M. Kim
The advancements of the Neural Radiance Field (NeRF) and its variants have demonstrated remarkable capabilities in generating photo-realistic novel views from a small set of input images. While recent works suggest various techniques and model architectures that enhance speed or reconstruction quality, little attention is paid to exploring the RGB color space of input images. In this paper, we propose
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CP-NeRF: Conditionally Parameterized Neural Radiance Fields for Cross-scene Novel View Synthesis Comput. Graph. Forum (IF 2.5) Pub Date : 2023-10-30 Hao He, Yixun Liang, Shishi Xiao, Jierun Chen, Yingcong Chen
Neural radiance fields (NeRF) have demonstrated a promising research direction for novel view synthesis. However, the existing approaches either require per-scene optimization that takes significant computation time or condition on local features which overlook the global context of images. To tackle this shortcoming, we propose the Conditionally Parameterized Neural Radiance Fields (CP-NeRF), a plug-in