We present FlowCapX, a physics-enhanced framework for flow reconstruction from sparse video inputs, addressing the challenge of jointly optimizing complex physical constraints and sparse observational data over long time horizons. Existing methods often struggle to capture turbulent motion while maintaining physical consistency, limiting reconstruction quality and downstream tasks. Focusing on velocity inference, our approach introduces a hybrid framework that strategically separates representation and supervision across spatial scales. At the coarse level, we resolve sparse-view ambiguities via a novel optimization strategy that aligns long-term observation with physics-grounded velocity fields. By emphasizing vorticity-based physical constraints, our method enhances physical fidelity and improves optimization stability. At the fine level, we prioritize observational fidelity to preserve critical turbulent structures. Extensive experiments demonstrate state-of-the-art velocity reconstruction, enabling velocity-aware downstream tasks, e.g., accurate flow analysis, scene augmentation with tracer visualization and re-simulation.

翻译：我们提出FlowCapX，一种基于物理增强的稀疏视频输入流场重建框架，旨在解决在长时间尺度上联合优化复杂物理约束与稀疏观测数据的挑战。现有方法通常在捕捉湍流运动的同时难以保持物理一致性，从而限制了重建质量及下游任务性能。聚焦于速度场推断，我们的方法引入了一种混合框架，策略性地将表征与监督分离至不同空间尺度。在粗粒度层面，我们通过一种新颖的优化策略解决稀疏视角歧义，该策略将长期观测与基于物理的速度场对齐。通过强调基于涡度的物理约束，我们的方法提升了物理保真度并改善了优化稳定性。在细粒度层面，我们优先保证观测保真度以保留关键的湍流结构。大量实验表明，该方法实现了最先进的速度场重建，支持速度感知的下游任务，例如精确的流场分析、结合示踪剂可视化与重模拟的场景增强。