FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds
FoleyCrafter integrates pre-trained text-to-audio models with semantic adapters and temporal controllers for high-quality, synchronized video sound synthesis.
Key Findings
Methodology
FoleyCrafter combines a pre-trained text-to-audio model with a semantic adapter employing parallel cross-attention to align video semantics, and a temporal controller with onset detection and timestamp adaptation for precise synchronization. During training, only the adapter and controller are optimized, fixing the backbone model. Visual features are extracted via CLIP, integrated through cross-attention, and conditioned for audio generation. The temporal module predicts sound onset and adjusts timestamps to align audio with video frames, ensuring both semantic relevance and temporal accuracy.
Key Results
- On VGGSound, FoleyCrafter achieves a MKL score of 2.561 and FID of 19.67, outperforming prior methods like Diff-Foley (MKL 3.318). On AVSync15, the onset detection accuracy reaches 28.48%, demonstrating superior temporal synchronization. The model supports text prompts for controllable, diverse audio outputs, validated through extensive quantitative and qualitative evaluations.
- Ablation studies show the semantic adapter reduces MKL from 5.821 to 1.497, while the temporal controller improves onset detection accuracy from 26.65 to 28.48, confirming their effectiveness. The model maintains high audio fidelity and precise synchronization across various scenarios, with user studies confirming naturalness and relevance.
- The approach significantly advances video-to-audio synthesis by addressing quality and alignment issues simultaneously, enabling practical applications in film post-production, virtual reality, and gaming, with flexible controllability via text prompts.
Significance
This work bridges the gap between high-quality audio synthesis and precise video synchronization, a long-standing challenge in multimodal content creation. By leveraging pre-trained models and novel adaptation modules, FoleyCrafter enhances realism and controllability, facilitating automation in film, VR, and interactive media. Its ability to generate semantically relevant and temporally aligned sounds reduces reliance on manual Foley artistry, cutting costs and increasing efficiency. The framework’s flexibility for user control opens new avenues for personalized multimedia content, pushing the boundaries of AI-driven audiovisual synthesis.
Technical Contribution
The paper introduces a modular framework that extends pre-trained text-to-audio models with a semantic adapter based on parallel cross-attention, and a temporal controller comprising onset detection and timestamp adaptation modules. This design enables end-to-end training with fixed backbone models, preserving high audio quality while enhancing semantic relevance and synchronization. The integration of visual features into the conditioning process and the explicit temporal alignment mechanism represent key innovations, offering a flexible, controllable, and high-fidelity video-to-audio synthesis pipeline.
Novelty
This is the first work to effectively combine pre-trained text-to-audio models with video semantic and temporal alignment modules, enabling controllable, high-quality, synchronized video sound synthesis. Unlike prior methods relying solely on noisy datasets or post-processing, FoleyCrafter’s end-to-end learnable modules provide precise control and robustness, marking a significant step forward in multimodal generation.
Limitations
- The model's performance declines in complex, multi-source environments due to limited training data diversity. The temporal detector struggles with fast motion or blurry videos, affecting synchronization accuracy. High computational costs for training and inference limit real-time applications, and the reliance on large-scale paired datasets restricts scalability.
Future Work
Future research will focus on improving robustness in complex scenes, reducing computational costs, and integrating more sophisticated multimodal fusion techniques. Exploring unsupervised or semi-supervised training could broaden applicability. Additionally, enhancing real-time capabilities and expanding dataset diversity will be key to deploying FoleyCrafter in practical, large-scale multimedia production.
AI Executive Summary
FoleyCrafter represents a significant advancement in automatic video sound synthesis, addressing the longstanding challenge of producing high-quality, semantically relevant, and temporally synchronized audio for silent videos. Traditional methods often struggled with either audio fidelity or precise alignment, limiting their practical application in film, gaming, and virtual reality. This framework leverages the power of pre-trained text-to-audio models, augmented with a semantic adapter employing parallel cross-attention mechanisms to incorporate visual semantics, and a temporal controller with onset detection and timestamp regulation to ensure accurate synchronization.
The core innovation lies in integrating visual features extracted via CLIP into the audio generation process, allowing the model to produce sounds that are both contextually appropriate and tightly aligned with video events. During training, only the adapter and controller modules are optimized, preserving the high-quality audio capabilities of the backbone model. Extensive experiments on VGGSound and AVSync15 datasets demonstrate that FoleyCrafter outperforms existing approaches in both semantic relevance and synchronization accuracy, with MKL scores dropping to 2.561 and onset detection accuracy reaching 28.48%. The model also supports user prompts for controllable, diverse sound generation, enabling applications beyond static post-production.
This work has broad implications for multimedia content creation, reducing reliance on manual Foley artistry, lowering costs, and increasing production efficiency. Its flexible architecture allows integration into various platforms, from film editing to immersive VR experiences. Despite current limitations in complex multi-source environments and computational demands, future directions include enhancing robustness, real-time processing, and dataset diversity. Overall, FoleyCrafter paves the way for more intelligent, autonomous audiovisual synthesis, transforming how digital media is produced and experienced.
Deep Analysis
Background
视频配音作为影视制作中的关键环节，传统依赖人工Foley艺术家手工录制，成本高、效率低。近年来，深度学习推动了自动配音技术的发展，出现了如SpecVQGAN、Diff-Foley等模型，能自动生成音频，但在音质和时间同步方面仍存在瓶颈。预训练的文本到音频模型（如AudioLDM）在生成质量上取得突破，但难以直接应用于视频场景，主要因缺乏有效融合视频语义和时间信息的机制。多模态融合技术逐步兴起，试图结合视觉特征与文本条件，但仍面临语义相关性不足和同步不准的问题。本文旨在通过引入多模态适配机制，提升视频配音的质量和同步性。
Core Problem
现有方法在视频到音频生成中难以兼顾高质量和精确同步。噪声训练数据和复杂背景环境影响音频自然度，模型难以同时满足语义相关性和时间一致性。缺乏有效的机制将视觉信息融入音频生成，导致生成的声音与画面不匹配或不同步。这限制了自动配音在影视、虚拟现实等行业的应用，亟需一种能同时保证音质和同步的解决方案。
Innovation
本文提出FoleyCrafter，结合预训练的文本到音频模型，创新性引入平行交叉注意力机制实现视频语义对齐，设计时间检测与时间戳调节模块确保同步。模型结构支持多模态输入，允许文本引导和环境调节，突破了传统单一模型的局限。核心创新在于，将视觉特征通过交叉注意力融入音频生成流程，结合时间控制机制，实现高质量、同步且可控的配音效果。此设计兼顾语义相关性和时间同步，为多模态生成提供新思路。
Methodology
- �� 采用预训练的文本到音频模型（如AudioLDM）作为基础架构。• 设计语义适配器，利用平行交叉注意力机制，将视频特征（由CLIP提取）与音频条件结合，增强语义相关性。• 引入时间控制器，包括起点检测器（预测声音起点）和时间戳适配器（调节同步），实现精确时间对齐。• 训练过程中，固定基础模型，仅优化适配器和控制器，利用视频-音频配对数据进行端到端训练。• 结合多模态特征融合技术，支持文本提示引导和环境调节。• 最终模型可实现高质量、多样化、同步的配音生成。
Experiments
- �� 采用VGGSound和AVSync15两个公开数据集，评估模型在语义相关性和同步性方面的性能。• 设计对比实验，比较SpecVQGAN、Diff-Foley等方法的指标表现。• 使用MKL、FID、起点检测准确率等指标，全面衡量模型效果。• 进行消融实验，验证语义适配器和时间控制器的贡献。• 调整超参数，确保模型在多场景下的鲁棒性。• 用户评价验证生成的音频自然度和同步感。
Results
- �� 在VGGSound上，FoleyCrafter的MKL指标为2.561，优于Diff-Foley（3.318），FID降至19.67，表现出更高的音频质量。• 在AVSync15上，起点检测准确率达28.48%，优于对比模型，验证了时间同步能力。• 支持文本提示引导，实现多样化配音，用户可以调节音色和环境噪声，效果自然真实。• 消融实验显示，语义适配器显著提升语义相关性（MKL从5.821降至1.497），时间控制器改善同步（起点检测从26.65提升至28.48），验证了两个模块的有效性。
Applications
- �� 影视后期：自动生成符合场景的配音，降低制作成本。• 虚拟现实：增强虚拟场景的沉浸感，实现实时配音。• 游戏开发：快速生成多样化音效，提高开发效率。• 教育培训：制作互动多媒体内容，提升学习体验。未来还可结合虚拟主播、智能助手等，推动多模态内容的智能生成。
Limitations & Outlook
- �� 当前模型在复杂多源环境下表现有限，主要因训练数据偏向单一背景。• 时间检测在快速运动或模糊视频中准确率不足，影响同步。• 训练成本较高，依赖大量高质量配对数据，限制大规模应用。未来将优化模型鲁棒性，降低计算成本，提升多场景适应性。
Plain Language Accessible to non-experts
想象你在厨房准备一道菜。每种食材代表视频中的元素，比如蔬菜、肉、调料。厨师（模型）需要根据这些食材，搭配出合适的味道（声音）。如果厨师只知道食材名字（文本），可能做出不合适的菜；如果他能看到食材（视频内容）并知道什么时候放调料（时间点），就能做出更好吃、更合适的菜。FoleyCrafter就像这个厨师，结合视觉信息和时间控制，做出既符合场景又同步的“菜肴”（声音），让视频更生动、更真实。
ELI14 Explained like you're 14
想象你在拍电影，里面没有声音。你想让它听起来像真的一样，比如枪声、狗叫声，甚至风声。以前，专业的声音师傅要花很多时间用工具手工录制这些声音，但现在我们用电脑让它自己“说话”。这个新方法就像给电脑装了个聪明的耳朵和嘴巴，它可以看到画面，知道什么时候需要声音，还能根据你的描述调节音色和环境。这样，电影就更逼真，观众也会觉得像是真的一样。这个技术还能用在游戏、虚拟现实里，让虚拟世界更生动有趣。
Abstract
We study Neural Foley, the automatic generation of high-quality sound effects synchronizing with videos, enabling an immersive audio-visual experience. Despite its wide range of applications, existing approaches encounter limitations when it comes to simultaneously synthesizing high-quality and video-aligned (i.e.,, semantic relevant and temporal synchronized) sounds. To overcome these limitations, we propose FoleyCrafter, a novel framework that leverages a pre-trained text-to-audio model to ensure high-quality audio generation. FoleyCrafter comprises two key components: the semantic adapter for semantic alignment and the temporal controller for precise audio-video synchronization. The semantic adapter utilizes parallel cross-attention layers to condition audio generation on video features, producing realistic sound effects that are semantically relevant to the visual content. Meanwhile, the temporal controller incorporates an onset detector and a timestampbased adapter to achieve precise audio-video alignment. One notable advantage of FoleyCrafter is its compatibility with text prompts, enabling the use of text descriptions to achieve controllable and diverse video-to-audio generation according to user intents. We conduct extensive quantitative and qualitative experiments on standard benchmarks to verify the effectiveness of FoleyCrafter. Models and codes are available at https://github.com/open-mmlab/FoleyCrafter.