提升文本生成视频的多样性，同时保持时间一致性。
Consistency-Preserving Diverse Video Generation
- 通过联合采样框架优化视频生成多样性。
- 在不依赖解码器反向传播的情况下，显著提升时序一致性和色彩自然度。
- 适合需要高质量多样视频输出的研究与应用者。
文本到视频生成成本高昂，通常每条提示仅生成少量样本。在低样本条件下，最大化批次价值需提高跨视频多样性。现有图像生成方法虽能提升多样性，但视频生成中常导致视频内时序不一致，且需昂贵的视频解码器反向传播。本文提出一种针对流匹配视频生成器的联合采样框架，在保持时序一致性的同时提升批量多样性。方法先进行驱动多样性的更新，再移除会降低时序一致性目标的成分。为避免图像空间梯度，两项目标均通过轻量级潜在空间模型计算，无需视频解码和解码器反向传播。在当前最先进的文本到视频流匹配模型上实验表明，本方法多样性接近强基准，同时显著改善时序一致性和色彩自然度。代码已开源：https://github.com/XinshuangL/Diverse-Video。
原文摘要 · Abstract (English)
Text-to-video generation is expensive, so only a few samples are typically produced per prompt. In this low-sample regime, maximizing the value of each batch requires high cross-video diversity. Recent methods improve diversity for image generation, but for videos they often degrade within-video temporal consistency and require costly backpropagation through a video decoder. We propose a joint-sampling framework for flow-matching video generators that improves batch diversity while preserving temporal consistency. Our approach applies diversity-driven updates and then removes only the components that would decrease a temporal-consistency objective. To avoid image-space gradients, we compute both objectives with lightweight latent-space models, avoiding video decoding and decoder backpropagation. Experiments on a state-of-the-art text-to-video flow-matching model show diversity close to strong joint-sampling baselines while substantially improving temporal consistency and color naturalness. Our code is available at https://github.com/XinshuangL/Diverse-Video.
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