基于单目视频的细节增强实时可动画2DGS-Avatars
Real-Time Animatable 2DGS-Avatars with Detail Enhancement from Monocular Videos
AI总结：
提出基于2DGS的可动画人体虚拟形象实时重建框架，结合全局SMPL姿态参数与旋转补偿网络(RCN)，有效解决非刚性变形和姿态过渡问题，在重建质量和动画稳健性上超越现有方法。
摘要：
从单目视频中重建高质量、可动画的3D人体虚拟形象具有减少对复杂硬件依赖的巨大潜力，在游戏开发、增强现实和社交媒体中极具实用价值。然而，现有方法在捕捉精细几何细节和保持动画稳定性方面仍面临巨大挑战，尤其是在动态或复杂姿态下。为了解决这些问题，我们提出了一种基于2D Gaussian Splatting (2DGS)的可动画人体虚拟形象重建的新型实时框架。通过利用2DGS和全局SMPL姿态参数，我们的框架不仅对齐了位置和旋转偏差，还能实现重建虚拟形象的稳健且自然的姿态驱动动画。此外，我们引入了Rotation Compensation Network (RCN)，通过将局部几何特征与全局姿态参数相结合来学习旋转残差。该网络显著改善了对非刚性变形的处理，并确保了动画过程中平滑、无伪影的姿态过渡。实验结果表明，我们的方法成功从单目视频中重建了逼真且高度可动画的人体虚拟形象，有效保留了细粒度细节，同时确保了稳定自然的姿态变化。我们的方法在公开基准上的重建质量和动画稳健性方面均超越了当前最先进的方法。
英文摘要：
High-quality, animatable 3D human avatar reconstruction from monocular videos offers significant potential for reducing reliance on complex hardware, making it highly practical for applications in game development, augmented reality, and social media. However, existing methods still face substantial challenges in capturing fine geometric details and maintaining animation stability, particularly under dynamic or complex poses. To address these issues, we propose a novel real-time framework for animatable human avatar reconstruction based on 2D Gaussian Splatting (2DGS). By leveraging 2DGS and global SMPL pose parameters, our framework not only aligns positional and rotational discrepancies but also enables robust and natural pose-driven animation of the reconstructed avatars. Furthermore, we introduce a Rotation Compensation Network (RCN) that learns rotation residuals by integrating local geometric features with global pose parameters. This network significantly improves the handling of non-rigid deformations and ensures smooth, artifact-free pose transitions during animation. Experimental results demonstrate that our method successfully reconstructs realistic and highly animatable human avatars from monocular videos, effectively preserving fine-grained details while ensuring stable and natural pose variation. Our approach surpasses current state-of-the-art methods in both reconstruction quality and animation robustness on public benchmarks.
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。
