Flux4D: Flow-based Unsupervised 4DReconstruction

Reconstructing large-scale dynamic scenes from visual observations is a fundamental challenge in computer vision. While recent differentiable rendering methods such as NeRF and 3DGS have achieved impressive photorealistic reconstruction, they suffer from scalability limitations and require annotations to decouple moving actors from the static scene, such as in autonomous driving scenarios. Existing selfsupervised methods attempt to eliminate explicit annotations by leveraging motion cues and geometric priors, yet they remain constrained by per-scene optimization and sensitivity to hyperparameter tuning. In this paper, we introduce Flux4D, a simple and scalable framework for 4D reconstruction of large-scale dynamic driving scenes. Flux4D directly predicts 3DGaussians and their motion dynamics to reconstruct sensor observations in a fully unsupervised manner. By adopting only photometric losses and enforcing an "as static as possible" regularization, Flux4D learns to decompose dynamic elements directly from raw data without requiring pre-trained supervised models or foundational priors simply by training across many scenes. Our approach enables efficient reconstruction of dynamic scenes within seconds, scales effectively to large datasets, and generalizes well to unseen environments, including rare and unknown objects. Experiments on outdoor driving datasets show Flux4D significantly outperforms existing methods in scalability, generalization, and reconstruction quality.