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ObjectiveClothed human generation, which aims to recover the 3D geometry and texture of the human body from input data to generate accurate 3D human models, is a challenging problem in the fields of computer vision and computer graphics. The need for high-quality generations has become increasingly critical with the growing demand for realistic 3D human models in applications such as virtual reality and augmented reality. Traditional multiview generation methods, which are often expensive and impractical for everyday use, typically require specialized equipment to capture images from multiple viewpoints. By contrast, obtaining single-view images from the web is much easier than obtaining multiview images. Thus, single-view generation methods become more cost-effective than multiview generation methods, and the model creation process becomes simple. Given these advantages, we consider using a single view as input to recover the 3D model of a clothed human. However, single-view images lack comprehensive spatial information and structural details of occluded regions. Thus, recovering a complete 3D shape becomes difficult. As a result, existing methods based on implicit functions struggle to learn rear-view information effectively, thereby leading to overly smooth and unrealistic back regions in the generated 3D human model. Methods combining diffusion models show some potential in enhancing texture detail performance. However, most of these methods lack view consistency constraints, thereby making the full recovery of the local texture details of the human body difficult. Additionally, the absence of precise geometric constraints during the diffusion process causes discrepancies between the generated models and the true geometry, particularly when handling complex 3D structures. Existing methods typically assume a uniform point distribution across spatial regions by ignoring variations in the distribution of query points caused by differences in distance from the human body surface. This assumption makes adapting to the geometric complexity differences across various regions of the body difficult for these methods. As a result, these methods face limitations when generating the surfaces of loose clothing, which have complex and variable geometries. This study addresses these challenges by combining three mechanisms： pose diffusion priors generation, multiview consistency constraints, and adaptive geometry generation. This approach not only preserves the generative capabilities of the diffusion model but also introduces geometric constraints to ensure the accuracy of the generation. Furthermore, this method can generate high-quality 3D human models by incorporating the probability distribution of human body structure. This study proposes a generation method that integrates pose diffusion priors with multiview consistency.MethodThis study constructs a method for single-view clothed human generation. First, a human pose estimation algorithm is used to extract 25 key points, which are encoded into Gaussian heatmaps to achieve spatial continuity modeling. This approach enables the model to understand the spatial relationships around the key points. The Gaussian heatmaps, combined with the human mask and UV mapping, are used to construct a pose feature vector. This feature vector guides the denoising process of the latent diffusion model and generates 2D diffusion images for unseen viewpoints through an adaptive cross-attention mechanism. Second, after the normal information of the （skinned multi-person linear model expressive, SMPLX） human template estimated from the input image and the 2D diffusion image are fused, they are input into the cross-view normal consistency network, where the multiview consistency mechanism extracts the corresponding 3D spatial features for each viewpoint. Finally, the voxelized features of the SMPLX human template and the 3D spatial features are fused and input into the distribution prediction network for spatial occupancy probability estimation. The model can express geometric uncertainty at different spatial locations and sample from the learned probability distribution by learning the distribution parameters of each point. Then, the 3D features, voxelized features, and sampling results are input into the occupancy prediction network to achieve 3D clothed human generation. Our entire model is trained on the THuman2.0 （Tsinghua human 2.0 dataset） dataset, with 490 images being used for training and 21 images being used for testing. We tested the model on the CAPE （clothed auto-person encoding） dataset to evaluate the generalization ability of the model further. This dataset is divided into two subsets： CAPE fitted poses （CAPE-FP）, which contains 75 images used to assess the geometric generation accuracy of the method under simple poses, and CAPE nonfitted poses （CAPE-NFP）, which contains 75 images and focuses on evaluating the method’s adaptability to complex poses. The experiments are conducted on an NVIDIA GeForce RTX 3090 GPU, with a learning rate being set to 1 × 10⁻4 and a batch size of 2.ResultWe conducted experiments on the THuman2.0 and CAPE datasets and compared the single-view clothed human generation results with the results of six other methods. Chamfer distance （CD） is used to evaluate the overall geometric similarity of the 3D human body, and point-to-surface distance （P2S） is used to assess the geometric accuracy of the reconstructed surface. Both metrics perform well when their values are small. On the THuman2.0 dataset, the CD and P2S metrics of the single-view clothed human generation method were reduced by 6.27% and 5.74%, respectively, compared with those of the best-performing method. On the CAPE-FP and CAPE-NFP subsets, the CD and P2S of the single-view clothed human generation method performed better than those of the other comparison methods. On the entire CAPE dataset, the CD metric of the single-view clothed human generation method decreased by an average of 8.67%, and the P2S metric decreased by an average of 2.38%. Quantitative experiments show that our method has good generalization ability for unseen data and can effectively handle human generation tasks in complex poses. Inference efficiency comparison results show that the computational complexity of our method is lower than that of similar diffusion model methods. Experimental results indicate that combining pose diffusion priors and multiview consistency helps recover the texture details of the 3D human body, and adaptive geometry generation enables accurate recovery of complex clothing topologies.ConclusionThe single-view 3D clothed human generation method proposed in this paper, which combines pose diffusion priors and multiview consistency, effectively recovers the local details of the clothed human and accurately generates 3D human models with complex topological structures, such as rich wrinkle details and loose clothing.… … 相似文献
《中国图象图形学报》2026,31(4):1256-1271
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近年来,利用安卓恶意应用实施的新型网络犯罪呈上升态势,现有方法在恶意应用行为解析的全面性、准确性及隐藏行为检出等方面存在不足,无法满足新型涉网案件快速侦办、有效打击的实战需求.基于此,提出一种新型安卓恶意应用行为细粒度解析框架：首先反编译安卓应用程序,以函数调用图(Functio… … 相似文献
史嘉琦;徐国天 《小型微型计算机系统》2026,(3)
130.
银行账户欺诈检测是安全行业中的一大难题,主要因为欺诈模式的快速变化和合法账户与欺诈账户之间的显著数据不平衡问题.传统检测方法在一定程度上能解决这一问题,但常常面临较高的误报率,并且在应对新型欺诈行为时表现较差.本文提出了一种创新方法,将专家模型(Mixture of Expert… … 相似文献
李子川;周志立;张龙 《小型微型计算机系统》2026,(3)
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近年来,深度强化学习(Deep Reinforcement Learning, DRL)技术在自动驾驶、智能机器人、金融交易等领域得到了广泛应用.然而,针对DRL智能体的黑盒对抗攻击仍然面临诸多挑战,例如计算成本高和迁移性有限等问题.为了解决上述问题,本文提出了一种新型的黑盒迁移… … 相似文献
陈晋音;翟建乐;陈思毅;王诚熠 《小型微型计算机系统》2026,(3)
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高性能科学计算是超级计算机的核心应用领域,包括粒子模拟、气候分析等关键任务.然而,随着摩尔定律逐渐失效,超级计算机体系结构日益趋向异构和复杂,导致科学计算应用的开发和优化变得更加困难.为解决这一问题,本文基于新一代申威超级计算平台,提出并实现了一种以数据为中心的并行编程模型——s… … 相似文献
沈沛祺;陈俊仕;安虹 《小型微型计算机系统》2026,(3)
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遥感图像描述技术是遥感领域的重要研究方向,能够对图像内容进行智能解析.然而,现有算法通常计算复杂度高、资源消耗大,难以在资源受限的终端或场合应用.为此,本文设计了一种轻量化遥感图像描述生成模型,旨在降低模型复杂度,同时保持描述生成的准确性.首先,在图像编码器中引入对比语言-图像预… … 相似文献
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步态识别作为一种远程生物特征识别技术,在医疗康复、刑侦侦查及社会治安等领域展现出广泛的应用前景.近年来,随着深度学习的快速发展,步态识别方法逐渐从传统的卷积神经网络(Convolutional Neural Network, CNN)转向更为先进的Transformer架构.尽管… … 相似文献
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锥形束计算机断层扫描(Cone Beam Computed Tomography, CBCT)是一种广泛用于医学领域的成像技术.基于高斯泼溅的R2-GS方法使用3D高斯表示CBCT三维图像,展现了出色的重建质量和实时的渲染速度.然而,R2-GS方法在重建区域边缘存在暗化现象,影响… … 相似文献
陈盛款;丁国平;丁佳骏 《小型微型计算机系统》2026,(3)
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为解决近场与远场信道估计方案在应对XL-MIMO混合场信道时的模型不匹配问题,以及克服现有信道估计算法在复杂混合场场景下的性能瓶颈与适用性限制,本文提出了一种双子空间贝叶斯优化算法.该算法基于稀疏信道假设,分别在角度域和极域对远场和近场信道分量进行估计,利用贝叶斯推断计算路径分量… … 相似文献
张家康;邓炳光;宋云鹏 《小型微型计算机系统》2026,(3)
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在人际沟通中;手势动作可以丰富语言信息;促进信息传递。语音驱动手势动作生成旨在通过语音输入条件;自动合成自然逼真且符合语境的手势动作序列。这一研究方向在计算机图形学和计算机视觉等领域受到广泛关注;并在电影动画制作、人机交互和虚拟现实等领域具有重要的应用价值。早期基于规则的方法效率… … 相似文献
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随着大语言模型在各领域的广泛应用;微调成为其适配特定任务的重要方法。当前主流的大模型微调方法主要分为部分微调和全量微调两种。部分微调虽能降低计算开销;但该方法仅更新大模型的少量参数;导致在复杂任务场景下;微调出的模型性能受限;虽然全量微调可以解决这一问题;但全量微调需要全面更新模… … 相似文献
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地址：北京海淀区中关村南四大街4号 邮编：100190 电话：010-62562563 E-mail ： cj_mail@jscas.ac.cn
本系统由:北京勤云科技发展有限公司提供技术支持
