OneFixer: High-Quality and Consistent One-Step Autoregressive 3DGS Refinement for Driving Scenes

Autoregressive video diffusion is a promising render-time fixer for 3D Gaussian Splatting (3DGS) in autonomous-driving simulation, but deployment demands high visual quality and temporal consistency at low latency. This is particu- larly challenging for one-step causal generation, where each imperfect predic- tion immediately becomes context for subsequent frames. Existing approaches improve test-time rollouts through staged training with multiple trained mod- ules and rollout-aware regularization, yet one-step refinement quality can still fall short of what render-time deployment requires. We introduce OneFixer, a one-step autoregressive video-diffusion fixer trained in one task-specific adap- tation stage. Our key idea is a deployment-matched shared rollout: the model’s own one-step predictions serve as the causal context for flow matching, exposing training to deployment-time errors, while the same rollout receives direct pixel- space perceptual supervision to preserve fine detail. Because the predictions op- timized for current-frame quality are exactly those reused as future context, fi- delity and autoregressive robustness are learned jointly, without bidirectional-to- causal conversion or teacher–student distillation. Targeting driving simulation, OneFixer further leverages cues that setting readily provides—lane geometry and dynamic-agent states—to improve geometric fidelity. Across Waymo and propri- etary driving scenes with 900-frame autoregressive rollouts, OneFixer achieves the lowest FVD, LPIPS, and DISTS among all baselines under one-step infer- ence, with strong geometric fidelity and temporal consistency matching or ex- ceeding multi-stage DMD pipelines. Under identical backbone and conditioning, it matches the final quality of a multi-stage DMD-with-Self-Forcing pipeline in under half the GPU-hours and continues to improve beyond its plateau. Project page: https://onefixer-web.vercel.app/