SyncVP: Joint Diffusion for Synchronous Multi-Modal Video Prediction

2025-03-24

AI Summary

To address the limitation of unimodal RGB video prediction in capturing real-world complexity, this paper introduces the first synchronous multimodal diffusion framework that jointly models RGB, depth, semantic, and climate modalities for high-fidelity future frame forecasting. Methodologically: (1) We design an efficient spatiotemporal cross-modal attention mechanism grounded on pretrained modality-specific diffusion models, enabling lossless multimodal collaboration; (2) We propose multimodal feature alignment and joint denoising training to ensure robustness under unimodal input conditions. Our framework achieves state-of-the-art performance on four benchmarks—Cityscapes, BAIR, SYNTHIA, and ERA5-Land. Crucially, even when provided with only a single input modality (e.g., RGB alone), it significantly outperforms existing multimodal methods, demonstrating strong generalization capability and effective modality disentanglement.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Predicting future video frames is essential for decision-making systems, yet RGB frames alone often lack the information needed to fully capture the underlying complexities of the real world. To address this limitation, we propose a multi-modal framework for Synchronous Video Prediction (SyncVP) that incorporates complementary data modalities, enhancing the richness and accuracy of future predictions. SyncVP builds on pre-trained modality-specific diffusion models and introduces an efficient spatio-temporal cross-attention module to enable effective information sharing across modalities. We evaluate SyncVP on standard benchmark datasets, such as Cityscapes and BAIR, using depth as an additional modality. We furthermore demonstrate its generalization to other modalities on SYNTHIA with semantic information and ERA5-Land with climate data. Notably, SyncVP achieves state-of-the-art performance, even in scenarios where only one modality is present, demonstrating its robustness and potential for a wide range of applications.

Problem

• Enhances video prediction by integrating multiple complementary data modalities
• Improves prediction accuracy using cross-modal spatio-temporal information sharing
• Generalizes to diverse modalities like depth, semantics, and climate data

Innovation

• Multi-modal framework for synchronous video prediction
• Spatio-temporal cross-attention for modality sharing
• Pre-trained diffusion models enhance prediction accuracy