FreeAnimate: Training-Free Human Image Animation with Preview-Guided Denoising
This work addresses the need for accessible and generalizable human image animation by eliminating the requirement for extensive training data and resources, making it easier to apply to diverse datasets.
FreeAnimate proposes a training-free framework for human image animation that uses preview-guided denoising to achieve temporal consistency, identity preservation, and background stability, outperforming existing training-free and training-based methods with quality comparable to state-of-the-art.
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/.