Human video generation remains challenging due to the difficulty of jointly modeling human appearance, motion, and camera viewpoint under limited multi-view data. Existing methods often address these factors separately, resulting in limited controllability or reduced visual quality. We revisit this problem from an image-first perspective, where high-quality human appearance is learned via image generation and used as a prior for video synthesis, decoupling appearance modeling from temporal consistency. We propose a pose- and viewpoint-controllable pipeline that combines a pretrained image backbone with SMPL-X-based motion guidance, together with a training-free temporal refinement stage based on a pretrained video diffusion model. Our method produces high-quality, temporally consistent videos under diverse poses and viewpoints. We also release a canonical human dataset and an auxiliary model for compositional human image synthesis. Code and data are publicly available at https://github.com/Taited/ReImagine.

翻译：人体视频生成因在有限多视角数据下联合建模人体外观、动作和相机视角存在困难而仍具挑战性。现有方法通常分别处理这些因素，导致可控性受限或视觉质量下降。我们从图像优先的角度重新审视该问题，即通过图像生成学习高质量人体外观，并将其作为视频合成的先验知识，从而实现外观建模与时间一致性的解耦。我们提出一种姿态与视角可控的流水线，该流水线结合预训练图像骨干网络与基于SMPL-X的运动引导，并引入基于预训练视频扩散模型的无训练时域精化阶段。我们的方法能够在多样化的姿态与视角下生成高质量且时间一致的视频。此外，我们还发布了一个规范人体数据集和一个用于组合式人体图像合成的辅助模型。代码与数据已公开于https://github.com/Taited/ReImagine。