从模糊视频重建可动画高保真3D人像，解决人体运动模糊难题
Deblur-Avatar: Animatable Avatars from Motion-Blurred Monocular Videos
- 基于人体运动轨迹建模模糊，联合优化3D高斯与运动路径
- 在真实和合成数据上显著提升重建质量，支持实时渲染
- 适合需要从模糊视频生成可动画角色的开发者和研究者
我们提出一种新框架，从运动模糊的单目视频中建模高保真、可动画的3D人体化身。运动模糊在真实动态视频采集中普遍存在，尤其在人体动作建模时更显著。现有方法或假设输入为清晰图像，无法处理运动模糊带来的细节丢失；或仅关注相机运动引起的模糊，忽略了在可动画化身中更常见的身体运动模糊。我们的方法将基于人体运动的运动模糊模型融入3D高斯点云（3DGS），通过显式建模曝光时间内的人体运动轨迹，联合优化轨迹与3D高斯分布，实现锐利高质量的人体化身重建。采用姿态依赖融合机制区分运动与静止区域，有效优化模糊与清晰区域。在合成与真实世界数据集上的大量实验表明，本方法在渲染质量和定量指标上显著优于现有方法，可在强运动模糊条件下生成清晰化身并支持实时渲染。代码与模型已开源：https://github.com/xianrui-luo/deblur_avatar。
原文摘要 · Abstract (English)
We introduce a novel framework for modeling high-fidelity, animatable 3D human avatars from motion-blurred monocular video inputs. Motion blur is prevalent in real-world dynamic video capture, especially due to human movements in 3D human avatar modeling. Existing methods either (1) assume sharp image inputs, failing to address the detail loss introduced by motion blur, or (2) mainly consider blur by camera movements, neglecting the human motion blur which is more common in animatable avatars. Our proposed approach integrates a human movement-based motion blur model into 3D Gaussian Splatting (3DGS). By explicitly modeling human motion trajectories during exposure time, we jointly optimize the trajectories and 3D Gaussians to reconstruct sharp, high-quality human avatars. We employ a pose-dependent fusion mechanism to distinguish moving body regions, optimizing both blurred and sharp areas effectively. Extensive experiments on synthetic and real-world datasets demonstrate that our method significantly outperforms existing methods in rendering quality and quantitative metrics, producing sharp avatar reconstructions and enabling real-time rendering under challenging motion blur conditions. Code and models are available at https://github.com/xianrui-luo/deblur_avatar.
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