FVVSplat: Feed-Forward Streamable Free-Viewpoint Video Reconstruction from Uncalibrated Inputs

Streamable Free-Viewpoint Video (FVV) reconstruction aims to recover dynamic 3D scenes from multi-view observations, requiring high-quality, on-the-fly reconstruction under strict constraints. Existing methods rely on known camera parameters and slow per-scene optimization, which hinders practical application. In this work, we introduce FVVSplat, a Feed-Forward Streamable Free-Viewpoint Video Reconstruction framework that instantly predicts dynamic 3D Gaussian Splatting representations from uncalibrated multi-view video streams. There are three key innovations are proposed. 1) an adaptive radius sampling mechanism for accurate Gaussian prediction from uncalibrated inputs; 2) a bidirectional deformation that ensures reliable, persistent Gaussians across frames and mitigates long-term error accumulation; 3) an efficient attention is integrated into both static prediction and dynamic deformation with linear complexity, substantially reducing computational cost while maintaining reconstruction quality across video resolutions. Extensive experiments on multiply streamable FVV reconstruction benchmarks demonstrate that FVVSplat significantly improves both reconstruction quality and inference time, with a 15x speedup and 2.16 dB PSNR gain over existing optimization-based methods on the N3DV dataset. Our source code and models will be available at http://github.com.