StreamCeption: Self-Correcting Latent Repair for Long Streaming Video Generation

Streaming video generation usually suffers from severe error accumulation when imperfectly generated latents are iteratively reused as fixed history. Inspired by variable-length generation paradigms like Flowception, we introduce StreamCeption, which adopts per-frame denoising and bidirectional attention but fundamentally shifts the generation paradigm. Instead of inserting latents into temporal gaps, StreamCeption consecutively appends noisy latents within a bounded active window, decoupling denoising completion from final frame output. While output frames become fixed history, the active window temporarily retains the oldest fully denoised latent. As subsequent trailing latents gain structural coherence during their denoising trajectories, a learned repair head adaptively determines whether to re-noise and refine this retained latent, correcting early errors before finalization. Trained alongside flow matching via self-sampled repair trials, our method uses reconstruction improvements to supervise repair selection and reconstruction loss to guide execution. Instantiated on the Wan2.2-TI2V-5B backbone, StreamCeption demonstrates the immense potential of this variable-length repair mechanism. Extensive experiments show that our approach scales effectively to this 5B model, achieving stronger long-horizon consistency than competitive streaming baselines while maintaining comparable visual quality, and seamlessly generalizing from an 81-frame training limit to highly coherent 162- and 320-frame videos with limited additional degradation over extended horizons.