HumANDiff: Articulated Noise Diffusion for Motion-Consistent Human Video Generation

• URL: http://arxiv.org/abs/2604.05961v1
• Date: Tue, 07 Apr 2026 14:55:10 GMT
• Title: HumANDiff: Articulated Noise Diffusion for Motion-Consistent Human Video Generation
• Abstract summary: HumANDiff enables human video generation by fine-tuning video diffusion models with articulated noise sampling.<n>It achieves motion-consistent, high-fidelity humans with diverse clothing styles.
• Score: 47.59225421284659
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite tremendous recent progress in human video generation, generative video diffusion models still struggle to capture the dynamics and physics of human motions faithfully. In this paper, we propose a new framework for human video generation, HumANDiff, which enhances the human motion control with three key designs: 1) Articulated motion-consistent noise sampling that correlates the spatiotemporal distribution of latent noise and replaces the unstructured random Gaussian noise with 3D articulated noise sampled on the dense surface manifold of a statistical human body template. It inherits body topology priors for spatially and temporally consistent noise sampling. 2) Joint appearance-motion learning that enhances the standard training objective of video diffusion models by jointly predicting pixel appearances and corresponding physical motions from the articulated noises. It enables high-fidelity human video synthesis, e.g., capturing motion-dependent clothing wrinkles. 3) Geometric motion consistency learning that enforces physical motion consistency across frames via a novel geometric motion consistency loss defined in the articulated noise space. HumANDiff enables scalable controllable human video generation by fine-tuning video diffusion models with articulated noise sampling. Consequently, our method is agnostic to diffusion model design, and requires no modifications to the model architecture. During inference, HumANDiff enables image-to-video generation within a single framework, achieving intrinsic motion control without requiring additional motion modules. Extensive experiments demonstrate that our method achieves state-of-the-art performance in rendering motion-consistent, high-fidelity humans with diverse clothing styles. Project page: https://taohuumd.github.io/projects/HumANDiff/

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