DP-DeGauss：动态概率高斯分解用于眼动4D场景重建
DP-DeGauss: Dynamic Probabilistic Gaussian Decomposition for Egocentric 4D Scene Reconstruction
AI总结：
本文提出DP-DeGauss方法，通过动态概率高斯分解实现眼动4D场景重建，解决动态第一视角场景重建中的复杂运动、遮挡和手-物体交互问题，实现背景、手部和物体的解耦。
摘要：
眼动视频对下一代4D场景重建至关重要，应用于AR/VR和具身AI。然而，重建动态第一视角场景具有挑战性，因为存在复杂的自我运动、遮挡和手-物体交互。现有分解方法不适用，假设固定视角或将动态合并到单一前景。为了解决这些限制，我们引入DP-DeGauss，一种用于眼动4D重建的动态概率高斯分解框架。我们的方法从COLMAP先验初始化统一的3D高斯集，为每个高斯添加可学习的类别概率，并动态路由到专门的变形分支用于背景、手部或物体建模。我们采用类别特定的遮罩以更好地解耦，并引入亮度和运动流控制以提高静态渲染和动态重建。大量实验表明，DP-DeGauss在PSNR上平均比基线高+1.70dB，同时SSIM和LPIPS也有提升。更重要的是，我们的框架实现了背景、手部和物体组件的首次和最先进解耦，使显式、细粒度分离成为可能，为更直观的自我场景理解和编辑铺平道路。
英文摘要：
Egocentric video is crucial for next-generation 4D scene reconstruction, with applications in AR/VR and embodied AI. However, reconstructing dynamic first-person scenes is challenging due to complex ego-motion, occlusions, and hand-object interactions. Existing decomposition methods are ill-suited, assuming fixed viewpoints or merging dynamics into a single foreground. To address these limitations, we introduce DP-DeGauss, a dynamic probabilistic Gaussian decomposition framework for egocentric 4D reconstruction. Our method initializes a unified 3D Gaussian set from COLMAP priors, augments each with a learnable category probability, and dynamically routes them into specialized deformation branches for background, hands, or object modeling. We employ category-specific masks for better disentanglement and introduce brightness and motion-flow control to improve static rendering and dynamic reconstruction. Extensive experiments show that DP-DeGauss outperforms baselines by +1.70dB in PSNR on average with SSIM and LPIPS gains. More importantly, our framework achieves the first and state-of-the-art disentanglement of background, hand, and object components, enabling explicit, fine-grained separation, paving the way for more intuitive ego scene understanding and editing.
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。
