FluxSplat: Feed-forward Gaussian Splatting for Streamable Dynamic View Synthesis from Monocular Videos

This paper addresses streamable dynamic view synthesis (DVS) from continuous monocular video streams using feed-forward dynamic Gaussian Splatting. Existing methods either process limited-length sequences as a whole or rely on adjacent frames, limiting spatial and temporal context and causing flickering in long streams. Our goal is to enable streamable DVS over long streams with limited resource consumption. We propose FluxSplat, a feed-forward framework that can incorporate historical information through a History-aware Depth Prediction Head for temporally consistent Gaussian prediction and enables compact Gaussian representation with a Compact Gaussian Adapter that allows high-resolution rendering with fewer hole artifacts. To efficiently handle long streams, we introduce Motion-aware Farthest View Sampling that selects informative frames to reduce both spatial and temporal redundancy. Experiments on benchmark datasets and in-the-wild videos show that FluxSplat achieves the state-of-the-art performance.