AutoRefiner: Improving Autoregressive Video Diffusion Models via Reflective Refinement Over the Stochastic Sampling Path
Published:Dec 12, 2025 01:28
•1 min read
•ArXiv
Analysis
The article introduces AutoRefiner, a method to enhance autoregressive video diffusion models. The core idea is to refine the video generation process by reflecting on the stochastic sampling path. This suggests an iterative improvement approach, potentially leading to higher quality video generation. The focus on autoregressive models indicates an interest in efficient video generation, and the use of diffusion models suggests a focus on high-fidelity generation. The paper likely details the specific refinement mechanism and provides experimental results demonstrating the improvements.
Key Takeaways
- •AutoRefiner is a method for improving autoregressive video diffusion models.
- •It refines video generation by reflecting on the stochastic sampling path.
- •The approach likely aims for higher quality and efficiency in video generation.
Reference
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