High-Fidelity, Long-Duration Human Image Animation with Diffusion Transformer
Paper#Computer Vision, Human Image Animation, Diffusion Models, Transformers🔬 Research|Analyzed: Jan 3, 2026 16:36•
Published: Dec 26, 2025 07:36
•1 min read
•ArXivAnalysis
This paper addresses key limitations in human image animation, specifically the generation of long-duration videos and fine-grained details. It proposes a novel diffusion transformer (DiT)-based framework with several innovative modules and strategies to improve fidelity and temporal consistency. The focus on facial and hand details, along with the ability to handle arbitrary video lengths, suggests a significant advancement in the field.
Key Takeaways
- •Proposes a DiT-based framework for high-fidelity and long-duration human image animation.
- •Addresses limitations in existing methods regarding long video generation and fine-grained details.
- •Introduces novel modules like hybrid guidance signals and a Position Shift Adaptive Module.
- •Employs a data augmentation strategy and skeleton alignment to handle shape variations.
- •Achieves superior performance compared to state-of-the-art approaches.
Reference / Citation
View Original"The paper's core contribution is a DiT-based framework incorporating hybrid guidance signals, a Position Shift Adaptive Module, and a novel data augmentation strategy to achieve superior performance in both high-fidelity and long-duration human image animation."