FreeAnimate: Training-Free Human Image Animation with Preview-Guided Denoising

2026-06-05

AI Summary

Existing training-free methods for human image animation struggle to balance generalization and generation quality. This work proposes a training-free animation framework that leverages pre-trained diffusion models, guiding the denoising process with preview frames to achieve pose alignment, identity preservation, and background stability. The key innovations include a novel preview-guided strategy that provides temporal and structural priors, along with Inversion-Boosted Attention and Reference-Anchored Self-Attention modules, which effectively enforce temporal consistency and identity retention. Experiments demonstrate that the proposed method outperforms existing training-free approaches and even surpasses several trained baselines across multiple datasets, achieving generation quality comparable to state-of-the-art models while exhibiting exceptional generalization capability.

Abstract

Human Image Animation has seen significant advancements, primarily driven by diffusion models. However, existing methods typically demand substantial training data and resources to achieve high-quality results, limiting generalization and accessibility. In this work, we introduce \emph{FreeAnimate}, a training-free framework that leverages the inherent capabilities of image diffusion models to enable temporal consistency, identity preservation, and background stability. Our approach incorporates a novel preview generation strategy that provides temporal and structural priors from generated preview frames, effectively guiding pose alignment and background consistency without training. Additionally, FreeAnimate introduces Inversion-Boosted Attention and Reference-Anchored Self-Attention modules to guarantee temporal consistency and identity preservation. Experimental results demonstrate that FreeAnimate outperforms existing training-free competitors and training-based baseline methods, achieving generation quality comparable to state-of-the-art methods and offering robust generalization across diverse datasets. Our project page is at https://freeani.github.io/.

Problem

• Human Image Animation
• diffusion models
• training-free
• temporal consistency
• identity preservation

Innovation

• training-free
• diffusion models
• temporal consistency
• preview-guided denoising
• human image animation