Envision4D: Envisioning Visual Futures via Feed-forward 4D Gaussian Splatting for Autonomous Driving

Forecasting the future evolution of dynamic scenes is crucial in autonomous driving. However, existing feed-forward Gaussian Splatting methods are primarily designed for scene interpolation. When extended to future extrapolation, they suffer from ghosting artifacts under large displacements and are constrained by simplified motion assumptions or strict future priors. To overcome these challenges, we propose Envision4D, a feed-forward framework for pose-free future extrapolation with self-supervised motion learning. Specifically, we introduce a Future Pose Prediction module that infers future camera parameters via an iterative refinement process. Furthermore, to capture non-linear dynamics, we propose In-layer Temporal Attention and employ Conditioned Motion Lifting, which transforms the highly uncertain extrapolation process into robust relational mappings. Finally, a Progressive Training Strategy is utilized to stabilize unsupervised motion learning against error accumulation. Extensive experiments demonstrate that Envision4D achieves state-of-the-art performance, significantly outperforming existing methods in future view synthesis.