Graphical Models最新文献_第2页

DP-Adapter: Dual-pathway adapter for boosting fidelity and text consistency in customizable human image generation DP-Adapter：用于在可定制的人类图像生成中提高保真度和文本一致性的双路径适配器

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-15 DOI: 10.1016/j.gmod.2025.101292

Ye Wang , Ruiqi Liu , Xuping Xie , Lanjun Wang , Zili Yi , Rui Ma

{"title":"DP-Adapter: Dual-pathway adapter for boosting fidelity and text consistency in customizable human image generation","authors":"Ye Wang , Ruiqi Liu , Xuping Xie , Lanjun Wang , Zili Yi , Rui Ma","doi":"10.1016/j.gmod.2025.101292","DOIUrl":"10.1016/j.gmod.2025.101292","url":null,"abstract":"<div><div>With the growing popularity of personalized human content creation and sharing, there is a rising demand for advanced techniques in customized human image generation. However, current methods struggle to simultaneously maintain the fidelity of human identity and ensure the consistency of textual prompts, often resulting in suboptimal outcomes. This shortcoming is primarily due to the lack of effective constraints during the simultaneous integration of visual and textual prompts, leading to unhealthy mutual interference that compromises the full expression of both types of input. Building on prior research that suggests visual and textual conditions influence different regions of an image in distinct ways, we introduce a novel Dual-Pathway Adapter (DP-Adapter) to enhance both high-fidelity identity preservation and textual consistency in personalized human image generation. Our approach begins by decoupling the target human image into visually sensitive and text-sensitive regions. For visually sensitive regions, DP-Adapter employs an Identity-Enhancing Adapter (IEA) to preserve detailed identity features. For text-sensitive regions, we introduce a Textual-Consistency Adapter (TCA) to minimize visual interference and ensure the consistency of textual semantics. To seamlessly integrate these pathways, we develop a Fine-Grained Feature-Level Blending (FFB) module that efficiently combines hierarchical semantic features from both pathways, resulting in more natural and coherent synthesis outcomes. Additionally, DP-Adapter supports various innovative applications, including controllable headshot-to-full-body portrait generation, age editing, old-photo to reality, and expression editing. Extensive experiments demonstrate that DP-Adapter outperforms state-of-the-art methods in both visual fidelity and text consistency, highlighting its effectiveness and versatility in the field of human image generation.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101292"},"PeriodicalIF":2.2,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time neural soft shadow synthesis from hard shadows 实时神经软阴影从硬阴影合成

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-14 DOI: 10.1016/j.gmod.2025.101294

Ran Chen , Xiang Xu , KaiYao Ge , Yanning Xu , Xiangxu Meng , Lu Wang

引用次数: 0

Feature line extraction based on winding number 基于圈数的特征线提取

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-13 DOI: 10.1016/j.gmod.2025.101296

Shuxian Cai , Juan Cao , Bailin Deng , Zhonggui Chen

引用次数: 0

GPU-accelerated rendering of vector strokes with piecewise quadratic approximation gpu加速绘制的矢量笔画与分段二次逼近

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-13 DOI: 10.1016/j.gmod.2025.101295

Xuhai Chen , Guangze Zhang , Wanyi Wang , Juan Cao , Zhonggui Chen

{"title":"GPU-accelerated rendering of vector strokes with piecewise quadratic approximation","authors":"Xuhai Chen , Guangze Zhang , Wanyi Wang , Juan Cao , Zhonggui Chen","doi":"10.1016/j.gmod.2025.101295","DOIUrl":"10.1016/j.gmod.2025.101295","url":null,"abstract":"<div><div>Vector graphics are widely used in areas such as logo design and digital painting, including both stroked and filled paths as primitives. GPU-based rendering for filled paths already has well-established solutions. Due to the complexity of stroked paths, existing methods often render them by approximating strokes with filled shapes. However, the performance of existing methods still leaves room for improvement. This paper designs a GPU-accelerated rendering algorithm along with a curvature-guided parallel adaptive subdivision method to accurately and efficiently render stroke areas. Additionally, we propose an efficient Newton iteration-based method for arc-length parameterization of quadratic curves, along with an error estimation technique. This enables a parallel rendering approach for dashed stroke styles and arc-length guided texture filling. Experimental results show that our method achieves average speedups of <span><math><mrow><mn>3</mn><mo>.</mo><mn>4</mn><mo>×</mo></mrow></math></span> for rendering quadratic stroked paths and <span><math><mrow><mn>2</mn><mo>.</mo><mn>5</mn><mo>×</mo></mrow></math></span> for rendering quadratic dashed strokes, compared to the best existing approaches.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101295"},"PeriodicalIF":2.2,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domain-Incremental Learning Paradigm for scene understanding via Pseudo-Replay Generation 通过伪回放生成的场景理解领域增量学习范式

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-11 DOI: 10.1016/j.gmod.2025.101290

Zhifeng Xie , Rui Qiu , Qile He , Mengtian Li , Xin Tan

{"title":"Domain-Incremental Learning Paradigm for scene understanding via Pseudo-Replay Generation","authors":"Zhifeng Xie , Rui Qiu , Qile He , Mengtian Li , Xin Tan","doi":"10.1016/j.gmod.2025.101290","DOIUrl":"10.1016/j.gmod.2025.101290","url":null,"abstract":"<div><div>Scene understanding is a computer vision task that involves grasping the pixel-level distribution of objects. Unlike most research focuses on single-scene models, we consider a more versatile proposal: domain-incremental learning for scene understanding. This allows us to adapt well-studied single-scene models into multi-scene models, reducing data requirements and ensuring model flexibility. However, domain-incremental learning that leverages correlations between scene domains has yet to be explored. To address this challenge, we propose a Domain-Incremental Learning Paradigm (D-ILP) for scene understanding, along with a new strategy of Pseudo-Replay Generation (PRG) that does not require manual labeling. Specifically, D-ILP leverages pre-trained single-scene models and incremental images for supervised training to acquire new knowledge from other scenes. As a pre-trained generation model, PRG can controllably generate pseudo-replays resembling source images from incremental images and text prompts. These pseudo-replays are utilized to minimize catastrophic forgetting in the original scene. We perform experiments with three publicly accessible models: Mask2Former, Segformer, and DeepLabv3+. With successfully transforming these single-scene models into multi-scene models, we achieve high-quality parsing results for original and new scenes simultaneously. Meanwhile, the validity and rationality of our method are proved by the analysis of D-ILP.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101290"},"PeriodicalIF":2.2,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Position-free multiple-scattering computations for micrograin BSDF model 微颗粒BSDF模型的无位置多次散射计算

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-05 DOI: 10.1016/j.gmod.2025.101288

Fangfang Zhou , Haiyu Shen , Mingzhen Li , Ying Zhao , Chongke Bi

{"title":"Position-free multiple-scattering computations for micrograin BSDF model","authors":"Fangfang Zhou , Haiyu Shen , Mingzhen Li , Ying Zhao , Chongke Bi","doi":"10.1016/j.gmod.2025.101288","DOIUrl":"10.1016/j.gmod.2025.101288","url":null,"abstract":"<div><div>Porous materials (e.g., weathered stone, industrial coatings) exhibit complex optical effects due to their micrograin and pore structures, posing challenges for photorealistic rendering. Explicit geometry models struggle to characterize their micrograin distributions at microscopic scales, while single-scattering microfacet model fails to accurately capture the multiple-scattering effects and causes energy non-conservation artifacts, manifesting as unrealistic luminance decay. We propose an enhanced micrograin BSDF model that accurately accounts for multiple scattering. First, we introduce a visible normal distribution function (VNDF) sampling method via rejection sampling. Building on VNDF sampling, we derive a position-free microsurface formulation incorporating both inter-micrograin and micrograin-to-base interactions. Furthermore, we propose a practical random walk method to simulate microsurface scattering, which accurately solves the derived formulation. Our micrograin BSDF model effectively eliminates the energy loss artifacts inherent in the previous model while significantly reducing noise, providing a physically accurate yet artistically controllable solution for rendering porous materials.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101288"},"PeriodicalIF":2.2,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Carvable packing of revolved 3D objects for subtractive manufacturing 用于减法制造的旋转三维物体的可切割包装

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-05 DOI: 10.1016/j.gmod.2025.101282

Chengdong Wei, Shuai Feng, Hao Xu, Qidong Zhang, Songyang Zhang, Zongzhen Li, Changhe Tu, Haisen Zhao

引用次数: 0

TerraCraft: City-scale generative procedural modeling with natural languages TerraCraft：使用自然语言的城市规模生成过程建模

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-05 DOI: 10.1016/j.gmod.2025.101285

Zichen Xi , Zhihao Yao , Jiahui Huang , Zi-Qi Lu , Hongyu Yan , Tai-Jiang Mu , Zhigang Wang , Qun-Ce Xu

{"title":"TerraCraft: City-scale generative procedural modeling with natural languages","authors":"Zichen Xi , Zhihao Yao , Jiahui Huang , Zi-Qi Lu , Hongyu Yan , Tai-Jiang Mu , Zhigang Wang , Qun-Ce Xu","doi":"10.1016/j.gmod.2025.101285","DOIUrl":"10.1016/j.gmod.2025.101285","url":null,"abstract":"<div><div>Automated generation of large-scale 3D scenes presents a significant challenge due to the resource-intensive training and datasets required. This is in sharp contrast to the 2D counterparts that have become readily available due to their superior speed and quality. However, prior work in 3D procedural modeling has demonstrated promise in generating high-quality assets using the combination of algorithms and user-defined rules. To leverage the best of both 2D generative models and procedural modeling tools, we present TerraCraft, a novel framework for generating geometrically high-quality 3D city-scale scenes. By utilizing Large Language Models (LLMs), TerraCraft can generate city-scale 3D scenes from natural text descriptions. With its intuitive operation and powerful capabilities, TerraCraft enables users to easily create geometrically high-quality scenes readily for various applications, such as virtual reality and game design. We validate TerraCraft’s effectiveness through extensive experiments and user studies, showing its superior performance compared to existing baselines.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101285"},"PeriodicalIF":2.2,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DDD++: Exploiting Density map consistency for Deep Depth estimation in indoor environments dddd++：利用密度图一致性在室内环境中进行深度估计

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-07-22 DOI: 10.1016/j.gmod.2025.101281

Giovanni Pintore , Marco Agus , Alberto Signoroni , Enrico Gobbetti

{"title":"DDD++: Exploiting Density map consistency for Deep Depth estimation in indoor environments","authors":"Giovanni Pintore , Marco Agus , Alberto Signoroni , Enrico Gobbetti","doi":"10.1016/j.gmod.2025.101281","DOIUrl":"10.1016/j.gmod.2025.101281","url":null,"abstract":"<div><div>We introduce a novel deep neural network designed for fast and structurally consistent monocular 360° depth estimation in indoor settings. Our model generates a spherical depth map from a single gravity-aligned or gravity-rectified equirectangular image, ensuring the predicted depth aligns with the typical depth distribution and structural features of cluttered indoor spaces, which are generally enclosed by walls, floors, and ceilings. By leveraging the distinctive vertical and horizontal patterns found in man-made indoor environments, we propose a streamlined network architecture that incorporates gravity-aligned feature flattening and specialized vision transformers. Through flattening, these transformers fully exploit the omnidirectional nature of the input without requiring patch segmentation or positional encoding. To further enhance structural consistency, we introduce a novel loss function that assesses density map consistency by projecting points from the predicted depth map onto a horizontal plane and a cylindrical proxy. This lightweight architecture requires fewer tunable parameters and computational resources than competing methods. Our comparative evaluation shows that our approach improves depth estimation accuracy while ensuring greater structural consistency compared to existing methods. For these reasons, it promises to be suitable for incorporation in real-time solutions, as well as a building block in more complex structural analysis and segmentation methods.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101281"},"PeriodicalIF":2.5,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sparse support path generation for multi-axis curved layer fused filament fabrication 多轴弯曲层熔丝制造的稀疏支撑路径生成

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-07-11 DOI: 10.1016/j.gmod.2025.101280

Tak Yu Lau , Dong He , Yamin Li , Yihe Wang , Danjie Bi , Lulu Huang , Pengcheng Hu , Kai Tang

{"title":"Sparse support path generation for multi-axis curved layer fused filament fabrication","authors":"Tak Yu Lau , Dong He , Yamin Li , Yihe Wang , Danjie Bi , Lulu Huang , Pengcheng Hu , Kai Tang","doi":"10.1016/j.gmod.2025.101280","DOIUrl":"10.1016/j.gmod.2025.101280","url":null,"abstract":"<div><div>In recent years, multi-axis fused filament fabrication has emerged as a solution to address the limitations of the conventional 2.5D printing process. By using a curved layering strategy and varying the print direction, the final parts can be printed with reduced support structures, enhanced surface quality, and improved mechanical properties. However, support structures in the multi-axis scheme are still needed sometimes when the support-free requirement conflicts with other constraints. Currently, most support generation algorithms are for the conventional 2.5D printing, which are not applicable to multi-axis printing. To address this issue, we propose a sparse and curved support filling pattern for multi-axis printing, aiming at enhancing the material efficiency by fully utilizing the bridge technique. Firstly, the overhang regions are detected by identifying the overhang points given a multi-axis nozzle path. Then, an optimization framework for the support guide curve is proposed to minimize its total length while ensuring that overhang filaments can be stably supported. Lastly, the support layer slices and support segments that satisfy the self-supported criterion are generated for the final support printing paths. Simulation and experiments have been performed to validate the proposed methodology.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101280"},"PeriodicalIF":2.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0