Drive2Gauss: FeedForward 4D Gaussian Reconstruction from Multimodal Generative Driving Videos
Generating reusable driving environments requires both controllable future observations and an explicit representation that supports rendering from different viewpoints, yet existing methods still recover explicit scenes from generated RGB videos through optimization for each sequence. To overcome these limitations, we present Drive2Gauss, a feedforward framework that converts jointly generated RGB, metric depth, and dynamic flow into dynamic 4D Gaussian scenes. A Multimodal Video Generator predicts the three modalities from reference multiview images and future control signals. Furthermore, we introduce a Gaussian Query Decoder that uses depth to initialize sparse 3D queries and flow to ...
| Authors | Tong Zhao, Zhiyuan Han, Lin Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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