Ani3DHuman: Photorealistic 3D Human Animation with Self-guided Stochastic Sampling

• URL: http://arxiv.org/abs/2602.19089v1
• Date: Sun, 22 Feb 2026 08:07:28 GMT
• Title: Ani3DHuman: Photorealistic 3D Human Animation with Self-guided Stochastic Sampling
• Authors: Qi Sun, Can Wang, Jiaxiang Shang, Yingchun Liu, Jing Liao,
• Abstract summary: We present Ani3DHuman, a framework that marries kinematics-based animation with video diffusion priors.<n>We first introduce a layered motion representation that disentangles rigid motion from residual non-rigid motion.<n>We propose a novel self-guided photorealistic sampling method, which effectively addresses the out-of-distribution problem.
• Score: 20.88616874056278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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