diff --git "a/2025/VAFlow_ Video-to-Audio Generation with Cross-Modality Flow Matching/layout.json" "b/2025/VAFlow_ Video-to-Audio Generation with Cross-Modality Flow Matching/layout.json"
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+ "content": "Gaoling School of Artificial Intelligence, Renmin University of China \nxihuaw@ruc.edu.cn, songruihua.bloon@outlook.com"
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+ "content": "Video-to-audio (V2A) generation aims to synthesize temporally aligned, realistic sounds for silent videos, a critical capability for immersive multimedia applications. Current V2A methods, predominantly based on diffusion or flow models, rely on suboptimal noise-to-audio paradigms that entangle cross-modal mappings with stochastic priors, resulting in inefficient training and convoluted transport paths. We propose VAFlow, a novel flow-based framework that directly models the video-to-audio transformation, eliminating reliance on noise priors. To address modality discrepancies, we employ an alignment variational autoencoder that compresses heterogeneous video features into audio-aligned latent spaces while preserving spatiotemporal semantics. By retaining cross-attention mechanisms between video features and flow blocks, our architecture enables classifier-free guidance within video source-driven generation. Without external data or complex training tricks, VAFlow achieves state-of-the-art performance on VGGSound benchmark, surpassing even textaugmented models in audio fidelity, diversity, and distribution alignment. This work establishes a new paradigm for V2A generation with a direct and effective video-to-audio transformation via flow matching."
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+ "content": "image or text-to-video tasks, where textual conditions are abstract and highly defined by human, the video conditions in V2A are more complex and require both spatial and temporal understanding. The generation goal also demands a high degree of alignment between the generated audio and the video conditions, not just semantically, but also temporally. There are two inherent difficulties: (1) different conditions on the same noise require the model to generate different audio outputs, demanding advanced condition processing [8, 41, 46], and (2) noise variations under the same condition lead to unstable generation results, prompting research into methods for noise control [44], such as finding a \"golden noise\" for consistent results [47]."
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+ "content": "In contrast, we propose a new perspective by shifting focus to the first step of the generation process. We question: If there are better priors than Gaussian noise? Given that many diffusion and FM models dedicate significant effort to integrating video conditions into the denoising process for better generation performance, we further question: If V2A can work better when directly transforming the video distribution to audio rather than from noise? Fortunately, both answers are affirmative."
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+ "content": "We introduce VAFlow, a novel framework for V2A generation, which directly denoises from the video space to the audio space. To implement this video-to-audio paradigm, we adopt the FM framework, as it theoretically supports any source distribution. There are three technical challenges for this implementation: (1) aligning source and target distributions: video and audio feature spaces typically have different time and spatial resolutions, yet FM requires the source and target distributions to be aligned (the same shape). (2) uncertainty of the feature type: identifying the optimal video representations/feature types as the source distribution. (3) conditioning mechanism: exploring additional video information integration ways beyond encoding it in the source distribution. In VAFlow, we first employ an alignment VAE to adjust video features to match the audio latent space. It then explores three different video feature types (semantic CLIP [32], spatiotemporal CAVP [25], reconstruction-focused VidTok [38]) to systematically investigate source video information. Experiments show that using video features both as a source distribution and as conditioning information yields the best results for VAFlow."
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+ "content": "Diffusion models [17, 27, 35] reconstruct data distributions by iteratively denoising Gaussian-sampled latents, governmented by various designed schedulers achieving state-of-the-art performance in multimodal generation tasks. Unlike diffusions, flow-based models [1, 20, 22, 23] establish deterministic mappings between source (e.g., Gaussian) and target distributions through invertible neural transformations, with recent flow matching optimizing vector fields through optimal transport theory, enabling direct learning of straight transport paths between source and target distributions, thereby reducing training instability and inference steps. While both paradigms typically rely on Gaussian priors as their source distribution, existing video-to-audio systems on both paradigms model noise-to-audio generation conditioned on video features. In contrast, our approach directly aligns video latent spaces with target audio distributions via linear transformations under a flow matching framework, bypassing iterative noise modeling, preserving efficiency, and ensuring precise frame-to-frame alignment."
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+ "content": " representing an optional conditioning signal. Flow Matching (FM) [20, 23] establishes an inter-distribution transport process governed by the ordinary differential equation (ODE):"
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+ "content": ". The learned velocity field enables target generation by solving the ODE in Equation 1."
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+ "content": " can optionally be incorporated via cross-attention, detail discussion is conducted in the experiment Section 4.5."
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+ "content": ". Further studies (Section 4.5) demonstrate that this architectural choice is critical to enhance visual perception throughout the flow trajectory and improve audio generation quality."
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+ "content": "x_{v}^{\\mathrm{AE}} \\sim \\mathcal{N}(\\mu_{x_{v}^{\\mathrm{AE}}}, \\sigma_{x_{v}^{\\mathrm{AE}}})"
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| \\(FD_{pas.}\\)↓ | \\(FD_{mel.}\\)↓ | \\(IS_{mel.}\\)↑ | \\(KL_{pas.}\\)↓ | \\(KL_{mel.}\\)↓ |
| AR | SpecVQGAN [15] | RGB,Opti. | 342.4 | 18.15 | 20.5 | 3.10 | 6.84 | 55.9 | 14.1 |
| Im2Wav [34] | CLIP | 252.2 | 13.03 | 35.8 | 2.26 | 5.41 | 78.9 | 19.6 |
| Mask | VATT [24] | eva-CLIP+ | 154.1 | 7.07 | 64.3 | 1.34 | 3.46 | 84.8 | 25.0 |
| VAB [37] | eva-CLIP | 193.1 | 12.21 | 34.4 | 2.31 | 5.09 | 82.4 | 25.9 |
| Diffusion | MultiFoley [7] | CAVP | 241.6 | 13.00 | 67.7 | 1.59 | 3.34 | 85.2 | 27.0 |
| Seeing&Hearing [45] | ImageBind+ | 251.5 | 12.6 | 30.1 | 2.65 | 6.37 | 54.2 | 36.7 |
| Diff-Foley [25] | CAVP | 512.1 | 11.45 | 50.4 | 2.91 | 6.18 | 88.0 | 20.7 |
| LoVA [9] | CLIP | 149.1 | 6.90 | 58.7 | 2.10 | 5.12 | 87.0 | 26.3 |
| FoleyCrafter [46] | CLIP | 134.2 | 7.40 | 56.6 | 2.29 | 5.65 | 83.5 | 27.8 |
| TiVA [41] | CLIP | 111.9 | 4.31 | 67.0 | 2.02 | 5.21 | 87.5 | 30.3 |
| V2A-Mapper [40] | CLIP | 94.9 | 4.21 | 64.9 | 2.46 | 6.06 | 78.6 | 22.2 |
| Flow | MMAudio-L [8] | CLIP+ | 159.2 | 9.38 | 45.4 | 1.95 | 3.84 | 77.5 | 33.9 |
| Frieren [43] | CAVP | 259.9 | 6.70 | 52.4 | 2.92 | 5.64 | 97.0 | 23.0 |
| VAFlow (ours) | CLIP | 87.7 | 3.86 | 73.6 | 1.91 | 4.81 | 88.1 | 28.6 |
| VAFlow (ours) | CAVP | 91.8 | 4.73 | 64.8 | 2.41 | 5.96 | 96.3 | 25.1 |
| VAFlow (ours) | VidTok | 128.5 | 6.17 | 48.1 | 3.64 | 8.94 | 49.9 | 12.9 |
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