PoseAnimate: Zero-shot high fidelity pose controllable character animation
arxiv(2024)
摘要
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.
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