探索合成数据增强在可控人类中心视频生成中的作用
Exploring the Role of Synthetic Data Augmentation in Controllable Human-Centric Video Generation
AI总结：
本文研究合成数据对可控人类视频生成的影响，提出基于扩散的框架实现细粒度控制，并通过实验揭示合成与真实数据的互补作用，为高效选择合成样本提升视频真实感提供方法。
摘要：
可控人类视频生成旨在生成具有明确引导动作和外观的逼真人类视频，是数字人类、动画和具身AI的基础。然而，大规模、多样化且隐私安全的人类视频数据集稀缺，尤其对罕见身份和复杂动作构成瓶颈。合成数据提供了一种可扩展且可控的替代方案，但其在生成建模中的实际贡献仍因持续存在的Sim2Real差距而未被充分探索。本文系统研究了合成数据对可控人类视频生成的影响。我们提出了一种基于扩散的框架，使在外观和动作上实现细粒度控制，并提供一个统一的测试床，分析合成数据在训练过程中如何与真实世界数据相互作用。通过广泛实验，我们揭示了合成与真实数据的互补作用，并展示了可能的方法，以高效选择合成样本来增强运动真实感、时间一致性和身份保持。本研究为人类中心视频合成中合成数据的作用提供了首次全面探索，并为构建数据高效且可推广的生成模型提供了实用见解。
英文摘要：
Controllable human video generation aims to produce realistic videos of humans with explicitly guided motions and appearances,serving as a foundation for digital humans, animation, and embodied AI.However, the scarcity of largescale, diverse, and privacy safe human video datasets poses a major bottleneck, especially for rare identities and complex actions.Synthetic data provides a scalable and controllable alternative,yet its actual contribution to generative modeling remains underexplored due to the persistent Sim2Real gap.In this work,we systematically investigate the impact of synthetic data on controllable human video generation. We propose a diffusion-based framework that enables fine-grained control over appearance and motion while providing a unfied testbed to analyze how synthetic data interacts with real world data during training. Through extensive experiments, we reveal the complementary roles of synthetic and real data and demonstrate possible methods for efficiently selecting synthetic samples to enhance motion realism,temporal consistency,and identity preservation.Our study offers the first comprehensive exploration of synthetic data's role in human-centric video synthesis and provides practical insights for building data-efficient and generalizable generative models.
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。
