Image-to-video(I2V) generation aims to create a video sequence from a single
image, which requires high temporal coherence and visual fidelity with the
source image.However, existing approaches suffer from character appearance
inconsistency 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) Pose-Aware Control Module (PACM) incorporates diverse pose
signals into conditional embeddings, to preserve character-independent content
and maintain precise alignment of actions.2) Dual Consistency Attention Module
(DCAM) enhances temporal consistency, and retains character identity and
intricate background details.3) Mask-Guided Decoupling Module (MGDM) refines
distinct feature perception, 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.

从单张图片生成视频序列的 PoseAnimate 是一种新颖的零样本人物动画框架，通过将多样化的姿势信号整合到条件嵌入中以保持人物独立内容并维持动作的精确对齐，增强了时序一致性、保留了人物身份特征和复杂背景细节以及通过解耦角色和背景改善动画精度。实验结果表明，该方法在人物一致性和细节保真度方面优于现有的基于训练的方法，并且在所生成动画中始终保持较高水平的时序一致性。