EgoForce: 拡散フォーシングによる頑健なオンライン自己中心視点動作再構築
EgoForce: Robust Online Egocentric Motion Reconstruction via Diffusion Forcing
自己中心視点の映像から全身動作をオンラインで再構築する手法を提案。拡散フォーシングに基づく時間的非対称ノイズスケジュールで、ストリーミング入力に応じて逐次的にノイズを除去し、頑健かつリアルタイムな動作推定を実現。
著者: Inwoo Hwang, Donggeun Lim, Hojun Jang, Young Min Kim
分類: cs.CV
原文アブストラクト
With recent advances in embodied agents and AR devices, egocentric observations are readily available as input for real-world interactive online applications. However, egocentric viewpoints can only sporadically observe hands, in addition to the estimated head trajectory. We propose EgoForce, an online framework for reconstructing long-term full-body motion from noisy egocentric input. While existing generative frameworks can robustly handle noisy and sparse measurements, they assume a fixed-length observation window is available and are thus not suitable for real-time applications. Faster inference often relies on autoregressive prediction, sacrificing robustness. In contrast, we adopt a diffusion-based method with a temporally asymmetric noise schedule inspired by Diffusion Forcing. Specifically, our approach models temporally evolving uncertainty and incrementally denoises states as new streaming observations arrive. Combined with a noise-robust imputation strategy, EgoForce progressively generates stable and coherent full-body motion under strict causal constraints. Experiments demonstrate that our online framework outperforms existing online and offline methods, enabling long-horizon, full-body motion reconstruction in challenging egocentric scenarios.