基于行列式点过程引导策略优化的多样化视频生成

摘要

尽管当前文本到视频（T2V）扩散模型在生成质量与提示词对齐方面表现卓越，但在从单一文本提示生成多个视频时往往存在输出多样性不足的问题。我们将此挑战构建为集合层面的策略优化问题，旨在训练能够覆盖给定提示词对应多种合理结果的策略框架。为此，我们提出DPP-GRPO这一创新性多元视频生成框架，该框架融合行列式点过程（DPPs）与群组相对策略优化（GRPO）理论，通过对多样化生成结果施加显式奖励机制来解决多样性缺失问题。我们的方法通过DPP对冗余样本施加收益递减约束，同时借助GRPO对候选集合提供群组反馈，从而将多样性转化为显式优化信号。该框架具备即插即用和模型无关的特性，能在保持提示词忠实度与感知质量的同时，显著提升视频在视觉外观、摄像机运动及场景结构等方面的多样性。我们在WAN和CogVideoX模型上实现了该方法，实验表明在VBench、VideoScore等前沿基准测试及人工偏好研究中，本方法能持续提升视频多样性。此外，我们开源了代码并发布了包含3万条多样化提示词的新基准数据集，以支持后续研究。

English

While recent text-to-video (T2V) diffusion models have achieved impressive quality and prompt alignment, they often produce low-diversity outputs when sampling multiple videos from a single text prompt. We tackle this challenge by formulating it as a set-level policy optimization problem, with the goal of training a policy that can cover the diverse range of plausible outcomes for a given prompt. To address this, we introduce DPP-GRPO, a novel framework for diverse video generation that combines Determinantal Point Processes (DPPs) and Group Relative Policy Optimization (GRPO) theories to enforce explicit reward on diverse generations. Our objective turns diversity into an explicit signal by imposing diminishing returns on redundant samples (via DPP) while supplies groupwise feedback over candidate sets (via GRPO). Our framework is plug-and-play and model-agnostic, and encourages diverse generations across visual appearance, camera motions, and scene structure without sacrificing prompt fidelity or perceptual quality. We implement our method on WAN and CogVideoX, and show that our method consistently improves video diversity on state-of-the-art benchmarks such as VBench, VideoScore, and human preference studies. Moreover, we release our code and a new benchmark dataset of 30,000 diverse prompts to support future research.

基于行列式点过程引导策略优化的多样化视频生成

Diverse Video Generation with Determinantal Point Process-Guided Policy Optimization

摘要

Support