イベントカメラ誘導による動的人体とシーンの3Dガウシアンスプラッティング再構成
Event-guided 3D Gaussian Splatting for Dynamic Human and Scene Reconstruction
単眼イベントカメラ映像から、動く人体と静的シーンを3Dガウシアンスプラッティングで同時再構成する手法を提案。イベント誘導損失で高速運動時のブレを抑え、人体マスク不要で高精度を実現した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Xiaoting Yin, Hao Shi, Kailun Yang, Jiajun Zhai, Shangwei Guo, Lin Wang, Kaiwei Wang
分類: cs.CV, cs.RO, eess.IV
原文アブストラクト
Reconstructing dynamic humans together with static scenes from monocular videos remains difficult, especially under fast motion, where RGB frames suffer from motion blur. Event cameras exhibit distinct advantages, e.g., microsecond temporal resolution, making them a superior sensing choice for dynamic human reconstruction. Accordingly, we present a novel event-guided human-scene reconstruction framework that jointly models human and scene from a single monocular event camera via 3D Gaussian Splatting. Specifically, a unified set of 3D Gaussians carries a learnable semantic attribute; only Gaussians classified as human undergo deformation for animation, while scene Gaussians stay static. To combat blur, we propose an event-guided loss that matches simulated brightness changes between consecutive renderings with the event stream, improving local fidelity in fast-moving regions. Our approach removes the need for external human masks and simplifies managing separate Gaussian sets. On two benchmark datasets, ZJU-MoCap-Blur and MMHPSD-Blur, it delivers state-of-the-art human-scene reconstruction, with notable gains over strong baselines in PSNR/SSIM and reduced LPIPS, especially for high-speed subjects.
関連論文
- E-3DGS: 露光イベントと動きイベントを活用したガウシアンスプラッティング3D再構成/イベントカメラ
