无需训练即可生成任意长度高质量长视频。
Brick-Diffusion: Generating Long Videos with Brick-to-Wall Denoising
- 分段逐块去噪，模拟砌墙过程增强帧间联系。
- 生成视频质量显著优于现有方法，无明显退化。
- 适合需要长视频生成且无训练资源的场景。
扩散模型在文本驱动视频生成方面取得显著进展。然而，训练长视频生成模型需大量计算资源和数据，导致多数视频扩散模型仅限于少量帧。现有无需训练的方法利用预训练短视频扩散模型生成长视频时，常面临运动动态不足、视频保真度下降等问题。本文提出 Brick-Diffusion，一种新颖的无需训练方法，可生成任意长度的长视频。该方法引入砖块到墙体去噪策略：在潜空间中分段去噪，并在后续迭代中施加步长。这一过程模拟错缝砌墙结构，每块砖代表一个去噪片段，促进帧间信息交互，提升整体视频质量。定量与定性评估表明，Brick-Diffusion 在生成高保真视频方面优于现有基线方法。
原文摘要 · Abstract (English)
Recent advances in diffusion models have greatly improved text-driven video generation. However, training models for long video generation demands significant computational power and extensive data, leading most video diffusion models to be limited to a small number of frames. Existing training-free methods that attempt to generate long videos using pre-trained short video diffusion models often struggle with issues such as insufficient motion dynamics and degraded video fidelity. In this paper, we present Brick-Diffusion, a novel, training-free approach capable of generating long videos of arbitrary length. Our method introduces a brick-to-wall denoising strategy, where the latent is denoised in segments, with a stride applied in subsequent iterations. This process mimics the construction of a staggered brick wall, where each brick represents a denoised segment, enabling communication between frames and improving overall video quality. Through quantitative and qualitative evaluations, we demonstrate that Brick-Diffusion outperforms existing baseline methods in generating high-fidelity videos.
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