We present 4RC, a unified feed-forward framework for 4D reconstruction from monocular videos. Unlike existing approaches that typically decouple motion from geometry or produce limited 4D attributes such as sparse trajectories or two-view scene flow, 4RC learns a holistic 4D representation that jointly captures dense scene geometry and motion dynamics. At its core, 4RC introduces a novel encode-once, query-anywhere and anytime paradigm: a transformer backbone encodes the entire video into a compact spatio-temporal latent space, from which a conditional decoder can efficiently query 3D geometry and motion for any query frame at any target timestamp. To facilitate learning, we represent per-view 4D attributes in a minimally factorized form by decomposing them into base geometry and time-dependent relative motion. Extensive experiments demonstrate that 4RC outperforms prior and concurrent methods across a wide range of 4D reconstruction tasks.

翻译：本文提出4RC，一种用于单目视频四维重建的统一前馈框架。与现有方法通常将运动与几何解耦，或仅生成稀疏轨迹或双视角场景流等有限四维属性不同，4RC学习了一种能够同时捕获稠密场景几何与运动动态的整体四维表征。其核心在于引入了一种新颖的"一次编码、任意时空查询"范式：通过Transformer主干网络将整个视频编码至紧凑的时空潜在空间，条件解码器可从中高效查询任意目标时间戳下任意查询帧的三维几何与运动信息。为促进学习，我们将每视角四维属性分解为基础几何与时间相关相对运动，以最小化因子化形式进行表征。大量实验表明，4RC在广泛四维重建任务中均优于现有及同期方法。