Text-Conditioned Human Image Animation with Dynamic Temporal Representation Learning
DOI:
https://doi.org/10.66238/jaeee18Keywords:
Human Image Animation, Temporal Representation Learning, Text-Conditioned Generation, Video SynthesisAbstract
The synthesis of highly realistic human animations from static images driven by natural language prompts represents a significant frontier in generative computer vision. Current approaches frequently struggle with maintaining temporal consistency and structural fidelity, resulting in artifacts such as flickering, anatomical distortions, and semantic misalignment over extended sequences. To address these limitations, we introduce a novel framework centered on dynamic temporal representation learning tailored for text-conditioned human image animation. This paper presents an architecture that decouples spatial feature extraction from temporal dynamics, enabling the network to learn smooth motion priors directly guided by textual inputs. By integrating a dynamic temporal module that adaptively recalibrates motion features based on the semantic complexity of the text prompt, our approach ensures coherent frame-to-frame transitions while preserving the identity and structural integrity of the source human image. Extensive evaluations demonstrate that our framework significantly mitigates temporal degradation and outperforms contemporary methods across standard qualitative and quantitative metrics. The proposed methodology not only advances the theoretical understanding of temporal modeling in diffusion-based generative systems but also offers practical solutions for applications in digital media, virtual reality, and automated content creation.
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