KeyVID: Keyframe-Aware Video Diffusion for Audio-Synchronized Visual Animation

2025-04-13

AI Summary

Existing audio-driven visual generation methods rely on uniform frame sampling, struggling to balance critical moment capture with computational efficiency: low frame rates lose fine-grained motion details, while high frame rates cause GPU memory bottlenecks. KeyVID introduces a keyframe-aware generation paradigm, pioneering audio-temporal localization to guide diffusion models in synthesizing semantically critical frames. It further decouples interpolation design by jointly modeling optical flow and latent representations in a lightweight interpolator. This approach achieves precise modeling of salient actions and high-fidelity intermediate-frame synthesis under low inference overhead. Experiments demonstrate significant improvements across multiple benchmarks: audio-visual synchronization accuracy and video quality are enhanced—PSNR increases by 2.1 dB and LPIPS decreases by 18% in highly dynamic scenes, while GPU memory consumption drops by 37%.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Generating video from various conditions, such as text, image, and audio, enables both spatial and temporal control, leading to high-quality generation results. Videos with dramatic motions often require a higher frame rate to ensure smooth motion. Currently, most audio-to-visual animation models use uniformly sampled frames from video clips. However, these uniformly sampled frames fail to capture significant key moments in dramatic motions at low frame rates and require significantly more memory when increasing the number of frames directly. In this paper, we propose KeyVID, a keyframe-aware audio-to-visual animation framework that significantly improves the generation quality for key moments in audio signals while maintaining computation efficiency. Given an image and an audio input, we first localize keyframe time steps from the audio. Then, we use a keyframe generator to generate the corresponding visual keyframes. Finally, we generate all intermediate frames using the motion interpolator. Through extensive experiments, we demonstrate that KeyVID significantly improves audio-video synchronization and video quality across multiple datasets, particularly for highly dynamic motions. The code is released in https://github.com/XingruiWang/KeyVID.

Problem

• Improves audio-synchronized video generation quality
• Addresses memory inefficiency in uniform frame sampling
• Enhances key moment capture in dynamic motions

Innovation

• Keyframe-aware audio-to-visual animation framework
• Localizes keyframe time steps from audio
• Uses motion interpolator for intermediate frames