  Image-to-video (I2V) generation aims to create a video sequence from a single
image, which requires high temporal coherence and visual fidelity. However,
existing approaches suffer from inconsistency of character appearances and poor
preservation of fine details. Moreover, they require a large amount of video
data for training, which can be computationally demanding. To address these
limitations, we propose PoseAnimate, a novel zero-shot I2V framework for
character animation. PoseAnimate contains three key components: 1) a Pose-Aware
Control Module (PACM) that incorporates diverse pose signals into text
embeddings, to preserve character-independent content and maintain precise
alignment of actions. 2) a Dual Consistency Attention Module (DCAM) that
enhances temporal consistency and retains character identity and intricate
background details. 3) a Mask-Guided Decoupling Module (MGDM) that refines
distinct feature perception abilities, improving animation fidelity by
decoupling the character and background. We also propose a Pose Alignment
Transition Algorithm (PATA) to ensure smooth action transition. Extensive
experiment results demonstrate that our approach outperforms the
state-of-the-art training-based methods in terms of character consistency and
detail fidelity. Moreover, it maintains a high level of temporal coherence
throughout the generated animations.
