从单目视频学习显式连续运动表示以实现动态高斯点扩散
Learning Explicit Continuous Motion Representation for Dynamic Gaussian Splatting from Monocular Videos
AI总结：
本文提出了一种从单目视频生成高质量动态高斯点扩散的方法，通过SE(3) B样条运动基函数显式建模动态高斯的连续位置和姿态变形，并采用自适应控制机制提升计算效率和复杂运动建模能力。
摘要：
我们提出了一种从单目视频生成高质量动态高斯点扩散的方法。为此，我们在此工作中进一步超越了先前的方法，通过SE(3) B样条运动基函数显式建模动态高斯的连续位置和姿态变形，该基函数采用一组紧凑的控制点。为了在提高计算效率的同时增强对复杂运动的建模能力，设计了自适应控制机制以动态调整运动基和控制点的数量。此外，我们开发了一种软段重建策略以缓解长间隔运动干扰，并采用多视图扩散模型提供多视图线索以避免过度拟合训练视图。大量实验表明，我们的方法在新视角合成中优于最先进的方法。我们的代码可在https://github.com/hhhddddddd/se3bsplinegs获取。
英文摘要：
We present an approach for high-quality dynamic Gaussian Splatting from monocular videos. To this end, we in this work go one step further beyond previous methods to explicitly model continuous position and orientation deformation of dynamic Gaussians, using an SE(3) B-spline motion bases with a compact set of control points. To improve computational efficiency while enhancing the ability to model complex motions, an adaptive control mechanism is devised to dynamically adjust the number of motion bases and control points. Besides, we develop a soft segment reconstruction strategy to mitigate long-interval motion interference, and employ a multi-view diffusion model to provide multi-view cues for avoiding overfitting to training views. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in novel view synthesis. Our code is available at https://github.com/hhhddddddd/se3bsplinegs.
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。
