Consistency-Preserving Diverse Video Generation

• URL: http://arxiv.org/abs/2602.15287v1
• Date: Tue, 17 Feb 2026 01:12:20 GMT
• Title: Consistency-Preserving Diverse Video Generation
• Authors: Xinshuang Liu, Runfa Blark Li, Truong Nguyen,
• Abstract summary: We propose a joint-sampling framework for flow-matching video generators.<n>Our approach applies diversity-driven updates and then removes only the components that would decrease a temporal-consistency objective.<n>Experiments on a state-of-the-art text-to-video flow-matching model show diversity comparable to strong joint-sampling baselines.
• Score: 5.784739104479214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 comparable to strong joint-sampling baselines while substantially improving temporal consistency and color naturalness. Code will be released.

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