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

TL;DR

BBF tackles the challenge of context-aware video frame interpolation under fast, non-linear motion by introducing a multimodal conditioning framework that leverages audio-visual semantics. It builds on a DiT-based diffusion backbone with start–end frame constraints, decoupled cross-modal fusion, and a progressive training paradigm to balance global structure and local details, including region-focused supervision for facial and lip regions. Empirical results on DAVIS, HDTF, and Hallo3 show state-of-the-art performance across generic interpolation and audio-driven talking-head tasks, with strong human-evaluation and ablation results validating the contributions. The work offers a unified framework for multi-channel conditioned VFI and highlights avenues for future enhancement in contextual emotion modeling and multilingual generalization.

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.

Figures (4)

• Figure 1: The overall framework of BBF. Our model (left) is trained based on the proposed data processing pipeline (right). Built on a DiT-based backbone, the model introduces a decoupled multimodal fusion mechanism and adopts a progressively aligned training strategy. BBF enables video frame interpolation under various modality combinations.
• Figure 2: Qualitative comparison with video frame interpolation methods on different scenarios. (a) Generic scene from DAVIS dataset showing a violin performance with large motion. (b) Talking face scene from HDTF dataset demonstrating audio-synchronized interpolation. Our method achieves better motion continuity, identity preservation, and temporal consistency compared to VFI methods.
• Figure 3: Qualitative evaluation against audio-driven talking head generation methods on HDTF. Our method produces sharper facial details, smoother motion, and better audio-lip synchronization while maintaining natural transitions with boundary frames.
• Figure 4: Human evaluation results among our proposed BBF and other SoTA methods.