Beyond Boundary Frames: Audio-Visual Semantic Guidance for Context-Aware Video Interpolation

2025-12-03

AI Summary

Video frame interpolation suffers from inaccurate motion modeling and temporal inconsistency when handling fast, complex nonlinear motions—particularly critical in fine-grained tasks like audio-video synchronization. To address this, we propose a context-aware, multimodal-guidable interpolation framework. Methodologically, we adopt a DiT backbone and design a decoupled multimodal fusion mechanism supporting conditional inputs including text, audio, images, and videos. We introduce start-end frame difference embeddings to modulate sampling and loss computation, and employ a dynamically adjusted progressive multi-stage training strategy to enhance fine-grained motion modeling while preserving core generative capabilities. Experiments demonstrate that our method outperforms state-of-the-art approaches on both general frame interpolation and audio-video synchronized interpolation, achieving significant improvements in motion accuracy and temporal consistency. These results validate its effectiveness in cross-modal collaborative motion modeling and strong generalization across diverse modalities.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

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
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Handling fast, complex, and highly non-linear motion patterns has long posed challenges for video frame interpolation. Although recent diffusion-based approaches improve upon traditional optical-flow-based methods, they still struggle to cover diverse application scenarios and often fail to produce sharp, temporally consistent frames in fine-grained motion tasks such as audio-visual synchronized interpolation. To address these limitations, we introduce BBF (Beyond Boundary Frames), a context-aware video frame interpolation framework, which could be guided by audio/visual semantics. First, we enhance the input design of the interpolation model so that it can flexibly handle multiple conditional modalities, including text, audio, images, and video. Second, we propose a decoupled multimodal fusion mechanism that sequentially injects different conditional signals into a DiT backbone. Finally, to maintain the generation abilities of the foundation model, we adopt a progressive multi-stage training paradigm, where the start-end frame difference embedding is used to dynamically adjust both the data sampling and the loss weighting. Extensive experimental results demonstrate that BBF outperforms specialized state-of-the-art methods on both generic interpolation and audio-visual synchronized interpolation tasks, establishing a unified framework for video frame interpolation under coordinated multi-channel conditioning.

Problem

• Handles fast, complex, non-linear motion in video interpolation
• Improves sharpness and consistency in audio-visual synchronized tasks
• Unifies multi-modal conditioning for diverse interpolation scenarios

Innovation

• Audio-visual semantic guidance for context-aware interpolation
• Decoupled multimodal fusion mechanism with DiT backbone
• Progressive multi-stage training with dynamic loss weighting