E-Sem3DGS: Monocular Human and Scene Reconstruction via Event-Aided Semantic 3DGS

Reconstructing animatable humans, together with their surrounding static environments, from monocular, motion-blurred videos is still challenging for current neural rendering methods. Existing monocular human reconstruction approaches achieve impressive quality and efficiency, but they are designed for clean intensity inputs and mainly focus on the foreground human, leading to degraded performance under motion blur and incomplete scene modeling. Event cameras provide high temporal resolution and robustness to motion blur, making them a natural complement to standard video sensors. We present E-Sem3DGS, a semantically augmented 3D Gaussian Splatting framework that leverages hybrid event-intensity streams to jointly reconstruct explicit 3D volumetric representations of human avatars and static scenes. E-Sem3DGS maintains a single set of 3D Gaussians in Euclidean space, each endowed with a learnable semantic attribute that softly separates dynamic human and static scene content. We initialize human Gaussians from Skinned Multi-Person Linear (SMPL) model priors with semantic values set to 1 and scene Gaussians by sampling a surrounding cube with semantic values set to 0, then jointly optimize geometry, appearance, and semantics. To mitigate motion blur, we derive optical flow from events and use it to supervise image-based optical flow between rendered frames, enforcing temporal coherence in high-motion regions and sharpening both humans and backgrounds. On the motion-blurred ZJU-MoCap-Blur dataset, E-Sem3DGS improves the average full-frame PSNR from 21.75 to 32.56 (+49.7%) over previous methods. On MMHPSD-Blur, our method improves PSNR from 25.23 to 28.63 (+13.48%).