TL;DR
Stream-T1 introduces a test-time scaling framework for streaming video generation that reduces computational costs and enhances temporal coherence by leveraging chunk-level synthesis and historical information.
Contribution
It presents a novel TTS framework specifically designed for streaming video, incorporating three units to improve temporal dependency, coherence, and visual quality.
Findings
Significantly improves temporal consistency and motion smoothness.
Reduces computational overhead compared to existing methods.
Achieves superior visual quality on benchmark datasets.
Abstract
While Test-Time Scaling (TTS) offers a promising direction to enhance video generation without the surging costs of training, current test-time video generation methods based on diffusion models suffer from exorbitant candidate exploration costs and lack temporal guidance. To address these structural bottlenecks, we propose shifting the focus to streaming video generation. We identify that its chunk-level synthesis and few denoising steps are intrinsically suited for TTS, significantly lowering computational overhead while enabling fine-grained temporal control. Driven by this insight, we introduced Stream-T1, a pioneering comprehensive TTS framework exclusively tailored for streaming video generation. Specifically, Stream-T1 is composed of three units: (1) Stream -Scaled Noise Propagation, which actively refines the initial latent noise of the generating chunk using historically…
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