Consistency-Preserving Diverse Video Generation

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, long

Summary

A new joint-sampling framework addresses the challenge of generating diverse videos while preserving temporal consistency, particularly in computationally expensive text-to-video generation scenarios. The approach, designed for flow-matching video generators, applies diversity-driven updates and then selectively removes components that would degrade temporal consistency. This is achieved by computing both diversity and consistency objectives using lightweight latent-space models, thereby avoiding costly video decoding and backpropagation through a video decoder. Experiments conducted on the Wan 2.1 t2v-1.3B text-to-video model demonstrate that this method achieves cross-video diversity comparable to existing strong joint-sampling baselines, while significantly improving within-video temporal consistency and color naturalness.

Key takeaway

For Machine Learning Engineers optimizing text-to-video generation, this framework offers a solution to the diversity-consistency tradeoff. You can achieve high cross-video diversity without sacrificing temporal coherence or color naturalness by implementing latent-space gradient regulation. Consider integrating this flow-matching approach to improve batch utility and computational efficiency in your generative models, especially when working with limited compute budgets.

Key insights

A new flow-matching framework improves video diversity and temporal consistency using latent-space gradient regulation.

Principles

• Joint sampling enhances batch diversity.
• Latent-space objectives reduce computation.
• Gradient regulation balances diversity-consistency.

Method

The method applies diversity-driven updates, then removes components decreasing temporal consistency via gradient regulation. It uses lightweight latent-space models for objective computation, avoiding video decoder backpropagation.

In practice

• Use flow-matching for efficient video generation.
• Implement latent-space models for gradient computation.
• Apply gradient regulation to balance objectives.

Topics

• Video Generation
• Flow Matching
• Latent Space Models
• Temporal Consistency
• Diversity Enhancement
• Gradient Regulation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.