Training-Free Motion-Guided Video Generation with Enhanced Temporal Consistency Using Motion Consistency Loss

2025-01-13

AI Summary

This work addresses the problem of training-free motion-guided video generation. We propose a zero-shot motion-consistency optimization method based on diffusion models. Our core contribution is the first motion-consistency loss function, which explicitly models inter-frame correlations of a reference video within intermediate feature layers of the diffusion model. By backpropagating gradients through this loss in latent space, the method guides the initial noise sampling process to implicitly learn and reproduce the target motion pattern—without fine-tuning or additional training. The entire optimization requires only a single forward–backward pass. Experiments demonstrate significant improvements in temporal coherence and motion fidelity across diverse motion control tasks, establishing a new state-of-the-art benchmark for training-free video generation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

In this paper, we address the challenge of generating temporally consistent videos with motion guidance. While many existing methods depend on additional control modules or inference-time fine-tuning, recent studies suggest that effective motion guidance is achievable without altering the model architecture or requiring extra training. Such approaches offer promising compatibility with various video generation foundation models. However, existing training-free methods often struggle to maintain consistent temporal coherence across frames or to follow guided motion accurately. In this work, we propose a simple yet effective solution that combines an initial-noise-based approach with a novel motion consistency loss, the latter being our key innovation. Specifically, we capture the inter-frame feature correlation patterns of intermediate features from a video diffusion model to represent the motion pattern of the reference video. We then design a motion consistency loss to maintain similar feature correlation patterns in the generated video, using the gradient of this loss in the latent space to guide the generation process for precise motion control. This approach improves temporal consistency across various motion control tasks while preserving the benefits of a training-free setup. Extensive experiments show that our method sets a new standard for efficient, temporally coherent video generation.

Problem

• Video Generation
• Natural Motion Adaptation
• Training Efficiency

Innovation

• Motion Coherence Loss
• Gradient-guided Video Generation
• No-training Required