Video-to-Audio Generation Model

• Video-to-audio generation models are techniques that synthesize audio aligned with video content, facilitating automated post-production and enhancing synthetic media.
• They leverage advanced architectures—such as end-to-end diffusion models, autoregressive transformers, and text-to-audio modules—to capture semantic meaning and temporal nuances.
• Applications include scene-aware synthesis and controllable audio editing, with evaluations using metrics like FAD and AV-Align for fidelity and synchronization.

Video-to-Audio Generation Model

Video-to-audio (V2A) generation models synthesize temporally and semantically aligned audio from silent video inputs. These models enable automated post-production, enhance synthetic media, and present unique challenges at the intersection of computer vision, audio generation, and multimodal machine learning.

1. Problem Formulation and Model Taxonomy

A video-to-audio generation model aims to map a video input—commonly as a sequence of frames $v=\{f_1,\dots,f_n\}$—to a synthesized audio waveform $\hat a$ such that the resulting audio is temporally synchronized and semantically consistent with visual content. The mapping is generally denoted as: $v \longrightarrow \hat a$ V2A models can be broadly categorized along three dimensions:

• End-to-End Diffusion/Flow Models: Learn joint distributions or conditional flows between video and audio, typically through diffusion or continuous normalizing flows in a latent space (e.g., MMAudio, LoVA, Kling-Foley, Tri-Ergon, MGAudio) (Cheng et al., 2024, Ren et al., 2024, Wang et al., 24 Jun 2025, Li et al., 2024, Zhang et al., 28 Oct 2025).
• Semantic-Interface ("Scheme") Models: Decompose the problem into (a) extracting semantic description(s) from the video (optionally via an MLLM) and (b) conditioning text-to-audio generation using these intermediate prompts (e.g., SVA) (Chen et al., 2024).
• Autoregressive Transformers & Foundation Model Mappers: Translate visual features into audio token sequences or intermediate latents via autoregressive LLMs or lightweight mappers (e.g., DreamFoley, MFM-Mapper) (Li et al., 4 Dec 2025, Chen et al., 5 Sep 2025).

Major subvariants include selective/controllable V2A (e.g., SelVA with text-guided source selection), advanced scene detection, and editing-oriented models with audio alignment post-video editing (Lee et al., 2 Dec 2025, Yi et al., 2024, Ishii et al., 8 Dec 2025).

2. Core Architectural Components

V2A pipelines display a common modular structure, with differences in how visual understanding and temporal correlation are enforced.

Video Encoders

• Semantic/Global Features: Extracted with CLIP, MetaCLIP, or language-focused encoders (e.g., EVAClip-ViT-G), enabling strong scene or object semantics (Wang et al., 24 Jun 2025, Gramaccioni et al., 7 Oct 2025).
• Synchrony/Temporal Features: Encoders like CAVP, SyncFormer, and DINOv2 supply temporally rich embeddings optimized for motion and synchrony. Hierarchical encoders (e.g., TimeChat, Synchformer) capture fine-grained dynamic cues (Cheng et al., 2024, Chen et al., 5 Sep 2025, Wang et al., 24 Jun 2025).
• Dual-Role Encoders: Some models (MGAudio) unify the encoder's conditional and alignment role, supporting both conditioning of generative models and alignment supervision (Zhang et al., 28 Oct 2025).

Audio Representation and Decoders

• VAE/Codec-Encoded Latents: Common practice is to encode the waveform into a compressed latent space, enabling tractable diffusion or autoregressive training (e.g., Audio-VAE, Mel-VAE, RVQ) (Cheng et al., 2024, Li et al., 2024, Li et al., 4 Dec 2025).
• Text-to-Audio Modules: Pre-trained backbone models such as AudioGen, MusicGen, and AudioLDM provide robust text-conditioned synthesis for backgrounds or SFX (Chen et al., 2024, Chen et al., 5 Sep 2025).
• Mono/Stereo and High-Resolution: Advanced models (Tri-Ergon, Kling-Foley) offer high-fidelity 44.1 kHz stereo with spatial rendering (Li et al., 2024, Wang et al., 24 Jun 2025).

Multimodal Fusion and Conditioning

• Self/Cross-Attention: DiT or UNet-based transformers incorporate both video and (optional) text conditioning via cross-attention at multiple layers (Cheng et al., 2024, Gramaccioni et al., 7 Oct 2025, Ren et al., 2024).
• Adaptive/Positional Embeddings: Some models, especially for long-form or fine control, use adaptive LayerNorm, detailed positional encodings, or learned task-type embeddings for modality fusion (Li et al., 2024, Cheng et al., 2024).
• Intermediate Semantic Interface: In SVA, a multimodal LLM provides an interpretable audio generation scheme, used directly as an interface to text-to-audio models (Chen et al., 2024).

3. Training Objectives, Loss Functions, and Alignment Strategies

State-of-the-art models optimize for multimodal alignment, audio quality, and temporal consistency using various loss formulations:

• Diffusion/Flow-Matching Losses: The canonical loss is L2 denoising or score-matching on noisy latents, with conditional inputs being video and/or text features.

$$\mathcal{L}_{\mathrm{diff}} = \mathbb{E}_{z_0,\,\epsilon\sim\mathcal{N}(0,I),\,t}\big\| \epsilon - \epsilon_\theta(z_t, t, c)\big\|_2^2$$

or its flow-matching variant for continuous flows (Cheng et al., 2024, Zhang et al., 28 Oct 2025, Wang et al., 24 Jun 2025).

• Cross-Modal Alignment/Augmentation: Methods such as GRAM (parallelotope volume minimization over audio/video/text embeddings), explicit audio alignment losses, and onset-prediction tasks enforce semantic and temporal correspondence (Gramaccioni et al., 7 Oct 2025, Zhang et al., 28 Oct 2025, Ren et al., 2024).
• Data Augmentation and Self-augmentation: Detail-temporal masking, scene-mixing/auto-mixing, and fine-grained negative sampling are employed to prevent overfitting and increase robustness in alignment (Ishii et al., 8 Dec 2025, Lee et al., 2 Dec 2025, Cheng et al., 2024).
• Selective and Controllable Conditioning: Supplementary tokens ([SUP], as in SelVA), LUFS-based loudness embeddings (Tri-Ergon), and manual or predicted scene descriptors enable user-driven selectivity and fine-grained loudness or source control (Lee et al., 2 Dec 2025, Li et al., 2024).

4. Evaluation Protocols, Metrics, and Quantitative Performance

The evaluation of V2A models employs objective and subjective metrics to assess fidelity, semantic alignment, and synchronization:

Metric	Definition / Use	Comments
FAD	Fréchet Audio Distance between embeddings	Measures audio realism; common across models (Cheng et al., 2024).
FD	Fréchet Distance for distributions	Used with various embedding methods for generalization.
IS	Inception Score on audio class predictions	Evaluates semantic diversity and discriminability.
KL, MKL	(Mean) KL divergence between distributions	Assesses class distribution similarity, esp. in AV context.
CLAP/CLIP/IB	Cosine sim. (audio-text/video, ImageBind, etc.)	Semantic and multimodal alignment
AV-Align	Audio-visual temporal alignment metric	AV-specific, often from Synchformer-like models
DeSync	Offset in predicted temporal alignment	Lower values indicate better synchronization
MOS, Human Studies	Subjective audio quality (e.g., 1–5 Likert)	Used in combination with objective scores

For example, Tri-Ergon-L achieves FD=113.2, KL=1.82, and AV-Align=0.231 on VGGSound, surpassing prior models in both fidelity and alignment. SelVA outperforms prior SOTA on selective fidelity, achieving FAD=51.7, KAD=0.676, IS=13.07, and DeSync=0.721 on the VGG-MONOAUDIO benchmark (Li et al., 2024, Lee et al., 2 Dec 2025).

5. Specialized and Emerging Paradigms

• Long-Form Synthesis: LoVA demonstrates single-shot generation of high-consistency, long-duration audio (up to 60 s) using DiT with global attention, significantly outperforming UNet-based models prone to concatenation artifacts (Cheng et al., 2024).
• Scene-Aware Generation: Integration of scene boundary detection with per-segment synthesis addresses multi-scene challenges, as in Visual Scene Detector V2A models (Yi et al., 2024).
• Selective/Controllable Audio: Methods like SelVA allow source-level selection via prompt-guided video encoder modulation, facilitating professional compositing workflows (Lee et al., 2 Dec 2025).
• Stepwise Reasoning and Editing: ThinkSound leverages chain-of-thought MLLMs for multi-stage, interactive, and object-centric audio reasoning, enabling editing and context-dependent layering (Liu et al., 26 Jun 2025).
• Training-Free Inference: Multimodal Diffusion Guidance (MDG) applies joint embedding volume minimization as a plug-and-play guidance to any pretrained audio diffusion model, boosting alignment without retraining (Grassucci et al., 29 Sep 2025).
• Industry-Level Pipelines and Data: Kling-Foley and DreamFoley introduce large-scale codecs, dedicated audio evaluation benchmarks, dual encoders for multi-domain generalization, and highly scalable pipelines that unify text/video/audio modalities (Wang et al., 24 Jun 2025, Li et al., 4 Dec 2025).

6. Limitations and Directions for Future Research

Persistent gaps and research fronts include:

• Temporal Granularity: Models relying on single frames or coarse global semantics lack fine event alignment. End-to-end temporal reasoning remains an open goal (Chen et al., 2024, Cheng et al., 2024, Wang et al., 24 Jun 2025).
• Data Efficiency and Robustness: Despite the success of foundation models and mappers, fully data-efficient generalization without extensive paired data remains challenging (Chen et al., 5 Sep 2025).
• Scalability to Arbitrary Lengths: Memory and computational constraints limit the length and fidelity of audio that can be generated in a single pass; scalable attention or hierarchical methods are needed (Cheng et al., 2024, Li et al., 4 Dec 2025).
• Cross-Modal Generalization: Explicitly learning to synchronize objects, actions, and their corresponding audio signatures in open domains is an active area, particularly in zero-shot and few-shot settings (Zhang et al., 28 Oct 2025, Wang et al., 24 Jun 2025).
• Fine-Grained Control: Enhanced editability, source separation, and user personalization (loudness, style, mixing) are only partially addressed (e.g., Tri-Ergon, SelVA), and systematic interfaces for creative users are underdeveloped (Li et al., 2024, Lee et al., 2 Dec 2025).
• Automated Metrics: There is no universally adopted metric for AV-synchrony or semantic alignment; models report diverse metrics, often necessitating subjective analysis (Chen et al., 2024, Liu et al., 26 Jun 2025).

Significant future prospects comprise the coupling of video-to-audio generation with richer world models, task-specific evaluation datasets, continuous improvements in cross-modal LLMs, and integration into real-time or interactive pipelines.

• SVA: "Semantically consistent Video-to-Audio Generation using Multimodal Language Large Model" (Chen et al., 2024).
• LoVA: "LoVA: Long-form Video-to-Audio Generation" (Cheng et al., 2024).
• STA-V2A: "STA-V2A: Video-to-Audio Generation with Semantic and Temporal Alignment" (Ren et al., 2024).
• VTA-LDM: "Video-to-Audio Generation with Hidden Alignment" (Xu et al., 2024).
• MFM-Mapper: "Efficient Video-to-Audio Generation via Multiple Foundation Models Mapper" (Chen et al., 5 Sep 2025).
• DeepAudio-V1: "DeepAudio-V1" (Zhang et al., 28 Mar 2025).
• ThinkSound: "ThinkSound: Chain-of-Thought Reasoning in Multimodal LLMs for Audio Generation and Editing" (Liu et al., 26 Jun 2025).
• Tri-Ergon: "Tri-Ergon: Fine-grained Video-to-Audio Generation with Multi-modal Conditions and LUFS Control" (Li et al., 2024).
• Mel-QCD: "Synchronized Video-to-Audio Generation via Mel Quantization-Continuum Decomposition" (Wang et al., 10 Mar 2025).
• SelVA: "Hear What Matters! Text-conditioned Selective Video-to-Audio Generation" (Lee et al., 2 Dec 2025).
• MDG: "Training-Free Multimodal Guidance for Video to Audio Generation" (Grassucci et al., 29 Sep 2025).
• FoleyGRAM: "FoleyGRAM: Video-to-Audio Generation with GRAM-Aligned Multimodal Encoders" (Gramaccioni et al., 7 Oct 2025).
• MGAudio: "Model-Guided Dual-Role Alignment for High-Fidelity Open-Domain Video-to-Audio Generation" (Zhang et al., 28 Oct 2025).
• Kling-Foley: "Kling-Foley: Multimodal Diffusion Transformer for High-Quality Video-to-Audio Generation" (Wang et al., 24 Jun 2025).
• DreamFoley: "DreamFoley: Scalable VLMs for High-Fidelity Video-to-Audio Generation" (Li et al., 4 Dec 2025).