Current 3D human animation methods struggle to achieve photorealism: kinematics-based approaches lack non-rigid dynamics (e.g., clothing dynamics), while methods that leverage video diffusion priors can synthesize non-rigid motion but suffer from quality artifacts and identity loss. To overcome these limitations, we present Ani3DHuman, a framework that marries kinematics-based animation with video diffusion priors. We first introduce a layered motion representation that disentangles rigid motion from residual non-rigid motion. Rigid motion is generated by a kinematic method, which then produces a coarse rendering to guide the video diffusion model in generating video sequences that restore the residual non-rigid motion. However, this restoration task, based on diffusion sampling, is highly challenging, as the initial renderings are out-of-distribution, causing standard deterministic ODE samplers to fail. Therefore, we propose a novel self-guided stochastic sampling method, which effectively addresses the out-of-distribution problem by combining stochastic sampling (for photorealistic quality) with self-guidance (for identity fidelity). These restored videos provide high-quality supervision, enabling the optimization of the residual non-rigid motion field. Extensive experiments demonstrate that \MethodName can generate photorealistic 3D human animation, outperforming existing methods. Code is available in https://github.com/qiisun/ani3dhuman.

翻译：当前的三维人体动画方法难以实现照片级真实感：基于运动学的方法缺乏非刚性动力学（例如衣物动力学），而利用视频扩散先验的方法虽能合成非刚性运动，却存在质量伪影和身份信息丢失的问题。为克服这些局限，我们提出了Ani3DHuman框架，该框架将基于运动学的动画与视频扩散先验相结合。我们首先引入了一种分层运动表示，将刚性运动与残差非刚性运动解耦。刚性运动由运动学方法生成，随后产生粗渲染结果以引导视频扩散模型生成视频序列，从而恢复残差非刚性运动。然而，基于扩散采样的恢复任务极具挑战性，因为初始渲染结果处于分布外，导致标准的确定性ODE采样器失效。为此，我们提出了一种新颖的自引导随机采样方法，该方法通过结合随机采样（用于实现照片级真实感）与自引导机制（用于保持身份保真度），有效解决了分布外问题。这些恢复后的视频提供了高质量的监督信号，使得残差非刚性运动场的优化成为可能。大量实验表明，\MethodName能够生成照片级真实感的三维人体动画，其性能优于现有方法。代码发布于https://github.com/qiisun/ani3dhuman。