生成器-精炼器-检验器：一种用于从单目视频学习3D人体虚拟形象的三模块数据增强框架
Generator-Refiner-Examiner: A Tri-Module Data Augmentation Framework for 3D Human Avatar Learning from Monocular Videos
AI总结：
针对单目视频中3D人体虚拟形象重建的数据稀缺问题，提出由生成器、精炼器和检验器组成的三模块框架TrioMan，通过姿态和相机扰动生成多样样本、扩散模型精炼质量、注意力机制筛选一致样本，在X-Humans和NeuMan基准上超越现有方法。
摘要：
本文解决了从单目视频重建逼真且可动画化的3D人体虚拟形象的挑战。现有方法依赖于将逐主体优化与通用人体先验相结合，但在训练帧数有限时往往难以捕捉细粒度细节。为了缓解数据稀缺问题，我们提出了TrioMan，一个用于增强3D虚拟形象学习的系统性三模块框架。我们的方法包含三个协同组件。生成器通过对姿态和相机施加高斯扰动来创建多样化的未见样本。精炼器通过由纹理和几何线索引导的一步扩散来提高生成数据的质量。检验器使用基于双分支注意力的相似性评估来选择与主体一致的样本。在X-Humans和NeuMan基准上的实验表明，TrioMan优于最先进的方法。
英文摘要：
This paper addresses the challenge of reconstructing photorealistic and animatable 3D human avatars from monocular videos. While existing methods rely on combining per-subject optimization with generic human priors, they often fail to capture fine-grained details when training frames are limited. To mitigate this data scarcity, we propose TrioMan, a systematic tri-module framework for augmented 3D avatar learning. Our approach comprises three synergistic components. The Generator creates diverse unseen samples by imposing Gaussian perturbations on pose and camera. The Refiner improves the quality of generated data through one-step diffusion guided by texture and geometry cues. The Examiner selects subject-consistent samples using a dual-branch attention-based similarity evaluation. Experiments on the X-Humans and NeuMan benchmarks show that TrioMan outperforms state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
