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FlashRender tackles the discretization errors that cause camera-control drift in few-step generative video rendering by explicitly flattening the underlying denoising trajectory. The framework aligns source video latents with target-view geometry features from a frozen vision model, followed by MeanFlow fine-tuning and on-policy flow map distillation to correct rollout drift. This approach achieves a 25脳 reduction in sampling cost compared to multi-step diffusion baselines while matching visual fidelity and improving camera controllability on out-of-distribution trajectories.
Video diffusion models fail at few-step camera control not because of capacity limits, but because discretization errors bend the sampling trajectory鈥攁 failure mode resolved here to unlock 25脳 faster novel-view synthesis.
We present FlashRender, a few-step generative rendering framework that retakes a source video along a target camera trajectory in seconds. We identify sampling-step-dependent camera control as a prominent manifestation of discretization error in existing multi-step generative rendering models and show that resolving this inconsistency substantially lowers denoising trajectory curvature, facilitating subsequent step distillation. To this end, we introduce Representation Transformation and Alignment (RETA), which aligns hidden source-video representations with target-video features from a frozen visual geometry model. This directly encodes the geometric transformation within the source-video stream, enabling sampling-step-consistent camera control. We then fine-tune the model with the MeanFlow objective on the lower-curvature denoising trajectory induced by RETA, allowing the model to more effectively address discretization error. Finally, we apply on-policy flow map distillation to correct self-rollout errors under fixed few-step sampling. Extensive experiments show that RETA, MeanFlow, and on-policy flow map distillation play complementary roles in few-step generative rendering. Together, they enable our approach to match multi-step baselines in video quality and geometric consistency at 25x lower sampling cost while achieving superior camera controllability, even under out-of-distribution target camera trajectories.
