Animatable 3D reconstruction has significant applications across various fields, primarily relying on artists' handcraft creation. Recently, some studies have successfully constructed animatable 3D models from monocular videos. However, these approaches require sufficient view coverage of the object within the input video and typically necessitate significant time and computational costs for training and rendering. This limitation restricts the practical applications. In this work, we propose a method to build animatable 3D Gaussian Splatting from monocular video with diffusion priors. The 3D Gaussian representations significantly accelerate the training and rendering process, and the diffusion priors allow the method to learn 3D models with limited viewpoints. We also present the rigid regularization to enhance the utilization of the priors. We perform an extensive evaluation across various real-world videos, demonstrating its superior performance compared to the current state-of-the-art methods.
在各个领域中，可动画的3D重建具有重要的应用价值，主要依赖于艺术家的手工创建。最近，一些研究已成功地从单目视频中构建可动画的3D模型。然而，这些方法要求输入视频中的物体有足够的视角覆盖，并且通常需要大量的时间和计算成本进行训练和渲染。这一限制限制了它们的实际应用。在这项工作中，我们提出了一种从单目视频中使用扩散先验构建可动画的3D高斯平滑的方法。3D高斯表示显著加快了训练和渲染过程，扩散先验允许该方法学习有限视点的3D模型。我们还提出了刚性正则化来增强对先验的利用。我们对各种真实世界的视频进行了广泛的评估，展示了其相比当前最新技术方法的优越性能。