具有可证明解耦性的可控视频生成
Controllable Video Generation with Provable Disentanglement
AI总结：
针对现有可控视频生成方法忽略细粒度时空关系、控制精度与效率受限的问题，提出具有可证明解耦性的CoVoGAN框架，通过解耦静动态潜变量实现可控生成，实验验证其在生成质量和可控性上的显著提升。
摘要：
尽管近期在生成高质量且一致性的视频方面取得了进展，但可控视频生成仍然是一项重大挑战。大多数现有的可控视频生成方法将视频视为一个整体，忽略了复杂的细粒度时空关系，这限制了控制精度和效率。本文提出可控视频生成对抗网络（Controllable Video Generative Adversarial Networks，CoVoGAN）来解耦视频概念，从而实现对单个概念的高效独立控制。具体而言，遵循最小变化原则，我们首先解耦静态潜变量和动态潜变量；随后利用充分变化特性实现动态潜变量的分量级可辨识性，支持视频生成的解耦控制。为奠定理论基础，我们提供了严格分析，证明了该方法的可辨识性。基于这些理论洞见，我们设计了时序转换模块来解耦潜动态。为落实最小变化原则和充分变化特性，我们最小化潜动态变量的维度，并施加时序条件独立性约束。为验证方法有效性，我们将该模块作为插件集成到GAN中。在多个视频生成基准上开展的大量定性与定量实验表明，该方法在各类真实场景中显著提升了生成质量和可控性。
英文摘要：
Controllable video generation remains a significant challenge, despite recent advances in generating high-quality and consistent videos. Most existing methods for controlling video generation treat the video as a whole, neglecting intricate fine-grained spatiotemporal relationships, which limits both control precision and efficiency. In this paper, we propose Controllable Video Generative Adversarial Networks (CoVoGAN) to disentangle the video concepts, thus facilitating efficient and independent control over individual concepts. Specifically, following the minimal change principle, we first disentangle static and dynamic latent variables. We then leverage the sufficient change property to achieve component-wise identifiability of dynamic latent variables, enabling disentangled control of video generation. To establish the theoretical foundation, we provide a rigorous analysis demonstrating the identifiability of our approach. Building on these theoretical insights, we design a Temporal Transition Module to disentangle latent dynamics. To enforce the minimal change principle and sufficient change property, we minimize the dimensionality of latent dynamic variables and impose temporal conditional independence. To validate our approach, we integrate this module as a plug-in for GANs. Extensive qualitative and quantitative experiments on various video generation benchmarks demonstrate that our method significantly improves generation quality and controllability across diverse real-world scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
