Autoregression-free video prediction using diffusion model for mitigating error propagation

2025-05-28

AI Summary

To address error accumulation inherent in autoregressive (AR) paradigms for long-term video prediction, this paper proposes the first AR-free diffusion model framework, eliminating frame-by-frame generation and enabling end-to-end direct mapping from contextual frame tuples to future frame tuples. Our key contributions are: (1) a novel explicit motion-feature-driven motion prediction module that disentangles and models dynamic priors; and (2) a joint training strategy combining tuple-level generation with continuity regularization to ensure temporal coherence and contextual consistency. Evaluated on KTH and BAIR benchmarks, our method achieves significant improvements over state-of-the-art approaches—up to +1.2 dB in PSNR and +0.03 in SSIM for distant future frames—effectively mitigating error propagation while preserving both visual fidelity and temporal stability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Existing long-term video prediction methods often rely on an autoregressive video prediction mechanism. However, this approach suffers from error propagation, particularly in distant future frames. To address this limitation, this paper proposes the first AutoRegression-Free (ARFree) video prediction framework using diffusion models. Different from an autoregressive video prediction mechanism, ARFree directly predicts any future frame tuples from the context frame tuple. The proposed ARFree consists of two key components: 1) a motion prediction module that predicts a future motion using motion feature extracted from the context frame tuple; 2) a training method that improves motion continuity and contextual consistency between adjacent future frame tuples. Our experiments with two benchmark datasets show that the proposed ARFree video prediction framework outperforms several state-of-the-art video prediction methods.

Problem

• Mitigates error propagation in autoregressive video prediction
• Proposes AutoRegression-Free framework using diffusion models
• Improves motion continuity and contextual consistency

Innovation

• Autoregression-free video prediction framework
• Diffusion models for future frame prediction
• Motion and contextual consistency training