GaussianBody：基于3D高斯泼溅的穿衣人体重建
GaussianBody: Clothed Human Reconstruction via 3d Gaussian Splatting
AI总结：
针对动态穿衣人体重建中非刚性形变复杂、衣物细节难保留的问题，提出基于3D高斯泼溅的GaussianBody方法，通过姿态引导形变、物理先验、姿态优化及按尺度分裂机制，实现了当前最优的新视角渲染与显式几何重建效果。
摘要：
在本研究中，我们提出了一种名为GaussianBody的新型穿衣人体重建方法，该方法基于3D Gaussian Splatting（3D高斯泼溅）。与成本高昂的基于神经辐射场的模型相比，3D高斯泼溅近期在训练时长和渲染质量上均展现出优异性能。然而，由于存在复杂的非刚性形变与丰富的衣物细节，将静态3D高斯泼溅模型应用于动态人体重建问题并非易事。为应对这些挑战，我们的方法采用显式姿态引导形变，将规范空间与观测空间中的动态高斯关联起来，同时引入带正则化变换的物理先验，以缓解两个空间之间的歧义。在训练过程中，我们进一步提出了一种姿态优化策略来更新姿态回归，以补偿不准确的初始估计；还提出了一种按尺度分裂机制，以提升回归点云的密度。实验验证表明，我们的方法在动态穿衣人体的真实感新视角渲染任务上可达到当前最优水平，具备高质量细节，同时能实现显式几何重建。
英文摘要：
In this work, we propose a novel clothed human reconstruction method called GaussianBody, based on 3D Gaussian Splatting. Compared with the costly neural radiance based models, 3D Gaussian Splatting has recently demonstrated great performance in terms of training time and rendering quality. However, applying the static 3D Gaussian Splatting model to the dynamic human reconstruction problem is non-trivial due to complicated non-rigid deformations and rich cloth details. To address these challenges, our method considers explicit pose-guided deformation to associate dynamic Gaussians across the canonical space and the observation space, introducing a physically-based prior with regularized transformations helps mitigate ambiguity between the two spaces. During the training process, we further propose a pose refinement strategy to update the pose regression for compensating the inaccurate initial estimation and a split-with-scale mechanism to enhance the density of regressed point clouds. The experiments validate that our method can achieve state-of-the-art photorealistic novel-view rendering results with high-quality details for dynamic clothed human bodies, along with explicit geometry reconstruction.
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