Video-to-Audio generation with Hidden Alignment

## Abstract

Abstract Generating semantically and temporally aligned audio content in accordance with video input has become a focal point for researchers, particularly following the remarkable breakthrough in text-to-video generation. In this work, we aim to offer insights into the video-to-audio generation paradigm, focusing on three crucial aspects: vision encoders, auxiliary embeddings, and data augmentation techniques. Beginning with a foundational model built on a simple yet surprisingly effective intuition, we explore various vision encoders and auxiliary embeddings through ablation studies. Employing a comprehensive evaluation pipeline that emphasizes generation quality and video-audio synchronization alignment, we demonstrate that our model exhibits state-of-the-art video-to-audio generation capabilities. Furthermore, we provide critical insights into the impact of different data augmentation methods on enhancing the generation framework’s overall capacity. We showcase possibilities to advance the challenge of generating synchronized audio from semantic and temporal perspectives. We hope these insights will serve as a stepping stone toward developing more realistic and accurate audio-visual generation models.

## 1 Introduction

We humans perceive visual and audio input as two complementary aspects of our surroundings. When we witness an event, we instinctively expect to hear the corresponding sounds as we see it unfold. This integrated perception of sight and sound enhances our understanding and interpretation of the world around us. Modern video generation models, however, typically focus on generating visual content based on a set of textual prompts (Blattmann et al., ; Zeng et al., ; Bar-Tal et al., ). These models often lack the ability to incorporate audio information, resulting in outputs that appear as inferior imitations rather than realistic representations.

Our objective is to generate semantically and temporally-aligned audio content for a given silent video. This ambitious goal is rooted in extensive research conducted in the text-to-audio (TTA) domain, where researchers have proposed several robust baselines for audio generation (Kreuk et al., ; Liu et al., ; Ghosal et al., ). Furthermore, there have been recent attempts at video-to-audio (VTA) tasks (Sheffer & Adi, ; Luo et al., ; Wang et al., ). While these works have made significant strides, they still fall short of natural and arbitrary audio generation.

A common trait amongst these baseline models is their utilization of non-autoregressive generation frameworks, particularly Latent Diffusion Model (LDM) (Rombach et al., ). These models generate audio content by leveraging textual or visual features as generative conditions. However, the distinction between VTA and TTA presents two key challenges: 1) ensuring semantic coherence with the input condition, and 2) ensuring temporal alignment between the generated audio and the video. This is due to the necessity of accurately extracting and interpreting visual features that produce sounds, as well as identifying when these features generate sounds, even if they are continuously present in the scene.

Figure: Figure 1: Overview of the VTA-LDM framework. Given the silent video, our model generates semantically-related and temporally-aligned audios that accurately correspond to the visual events. The framework is based on a LDM with encoded vision features as the generation condition.
Refer to caption: x1.png

We try to provide insights into the VTA training paradigm by focusing on three key aspects: 1) the vision encoder, 2) the auxiliary embedding, and 3) the data augmentation. Vision encoder is responsible for extracting and interpreting visual features from the input video, capturing complex visual patterns that are integral to generating relevant audio content. It also has the potential to encode temporal information. Auxiliary embedding serves as a valuable source of additional contextual information for the model, e.g. textual description, position embedding, or other metadata associated with the video. Data augmentation can help improve the model’s generalization capabilities by introducing variations and perturbations in the training data. In particular, time-stretching of the training data can create fast-switching video scenes, which can push the model to better learn and understand temporal relations between visual and auditory information.

Our study initiates from training a foundational VTA model, denoted as VTA-LDM, drawing upon the successful text-to-audio LDM framework. By employing a Clip-based vision encoder (Radford et al., ), we concatenate frame-level video features temporally and map them using a projector as the generation condition. The overall framework of VTA-LDM is shown in fig. 1. The evaluation of these models concentrates on two primary aspects: semantic alignment and temporal alignment. Semantic alignment assesses the generated output’s semantic coherence and audio relevance, while temporal alignment gauges the synchronization between the generated audio and corresponding video frames. We then conduct comprehensive ablation experiments to examine the three key factors that can influence the overall performance. Results show that even our basic framework achieves state-of-the-art results in TTA tasks, generating semantically and partly-temporally aligned audio content based on visual input. Furthermore, the integration of additional features into the framework can significantly enhance the generation quality and the synchronization between audio and visual elements from various perspectives. For instance, the inclusion of extra textual prompts can aid in semantic understanding, positional embedding and concatenation augmentation can improve temporal alignment, and pretraining can enhance the overall quality of generation. These findings indicate that the overall framework empowers the model to learn and comprehend the dynamics of the scene and demonstrates the potential for further exploration and refinement.

- 1.
We introduce an effective VTA framework, named VTA-LDM, which attains state-of-the-art performance in VTA tasks.
- 2.
We investigate three fundamental aspects within the VTA paradigm, providing valuable insights into model design and training processes: 1) a linear combination of semantic video features is sufficient for encoding information required for aligned audio generation; 2) additional auxiliary embeddings assist in filtering out chaotic visual information and focusing on crucial elements; 3) pre-training enhances overall generation capability, and filtering high-quality data improves audio-video alignment.
- 3.
Through numerous ablation experiments and evaluations, we supply detailed quantitative results for VTA tasks.

We hope our experiments and results lay a solid foundation for future research in VTA field.

## 2 Related Work

### 2.1 Diffusion-based Generation

The success of audio generation tasks primarily stems from TTA diffusion models. These diffusion models were initially employed in image generation tasks (Ho et al., ; Dhariwal & Nichol, ; Nichol et al., ; Rombach et al., ), and then extended to audio and video generation (Liu et al., ; ; Singer et al., ; Blattmann et al., ; Brooks et al., ). Compared with previous works like Audiogen (Kreuk et al., ), which are predominantly based on regressive generative models, diffusion-based models excel in generation quality and diversity. For instance, Audioldm (Liu et al., ) learns continuous audio representations in the latent space. Tango (Ghosal et al., ) incorporates an instruction-tuned Large Language Model (LLM) FLAN-T5 to leverage its powerful representational capabilities. Other studies (Liu et al., ; Huang et al., ; Luo et al., ; Zeng et al., ; Xu et al., ) also utilize diffusion models, in conjunction with other enhancing methods, to improve the generation capacity.

### 2.2 Multimodal Audio Generation

Numerous studies have concentrated on multi-modal generation tasks related to the audio modality, extending beyond text and conducting significant experiments across various modalities. Previous research (Iashin & Rahtu, ), has focused on multi-class visually guided sound synthesis utilizing VQGAN (Esser et al., ). Recent work includes IM2WAV (Sheffer & Adi, ), which employs a two-stage audio generation pipeline to generate low-level audio representation autoregressively, followed by high-level audio tokens from images. Similarly, Diff-Foley (Luo et al., ) uses contrastive audio-visual pretraining to learn temporally aligned features for synthesizing audio from silent videos, drawing on LDM. Seeing&Hearing (Xing et al., ) and T2AV (Mo et al., ) show open-domain visual-audio generation capability based on similar multi-modality latent aligners. Other studies (Kurmi et al., ; Ruan et al., ; Yariv et al., ) have attempted joint video-audio generation using the same or aligned latent. Despite these advancements, generating synchronized audio that corresponds with a given video remains a significant challenge, particularly at the audio event level.

## 3 Baseline Framework

### 3.1 Overall Framework

Drawing inspiration from previous TTA works (Liu et al., ; Ghosal et al., ) and multi-modal research (Luo et al., ; Sheffer & Adi, ), we develop a fundamental VTA framework VTA-LDM consisting of several key components: a vision encoder, a conditional LDM, and a mel-spectrogram/audio variational auto-encoder (VAE). Specifically, we utilize vision features extracted from pre-trained vision encoders and feed them to the LDM as the generation condition through a linear projection layer. The LDM operates on the latent audio representation of the mel-spectrogram. The pre-trained audio VAE assists in decoding the denoised latent output to a mel-spectrogram, which is then fed to a vocoder to generate the final audio.

### 3.2 Vision Encoder

Vision Encoder is responsible for encoding not only the semantic meanings of the video $V$ but also the temporal information required for alignment with the generated audio. We employ pretrained vision encoders $f_{V}$ such as CLIP4CLIP (Luo et al., ) to extract the visual features from the input video. These features capture the essential visual information, including objects, actions, and scene context. We use a projection layer $\phi$ to map these features to the desired dimension of the diffusion condition. The vision encoders are all frozen during the training process, while the projection layer will be trained from scratch.

### 3.3 Latent Diffusion Model ( LDM )

Given the original input $x_{0}$, diffusion models follow a Markov chain of diffusion steps to gradually add random noise to data until it follows a Gaussian distribution ${N}(0,I)$, represented by $x_{t}$. The model then learns the reverse de-noising process to recover the original input data. For computational efficiency, LDM incorporates a trained perceptual compression model to encode the input $x$ into a low-dimensional latent space $Z$. In text-based generation tasks, the generation condition is often a given text description. In our VTA implementation, the condition $c$ is the projected video feature $\phi(f_{V})$. To perform sequential de-noising, we train a network$\epsilon_{\theta}$ to predict artificial noise, following the objective:

$$ $\min_{\theta}\mathbb{E}_{z_{0},\epsilon\sim\mathcal{N}(0,I),t\sim\text{Uniform }(1,T)}\lVert\epsilon-\epsilon_{\theta}(z_{t},t,\phi(f_{V}))\rVert_{2}^{2},$ (1) $$

where the condition $C=\phi(f_{V})$ is the projected visual embedding of the input video. We employ a classifier-free generation approach in the reverse process, which has been shown to be an effective method for text-guided generation and editing Ho & Salimans (). Given a latent variable and a textual prompt, the generation is performed both conditionally and unconditionally, and then weighted according to a given scale. Formally, let $\varnothing$ be the null text embedding, $w$ denotes the guidance scale, and the generation process can be defined by:

$$ $\tilde{\epsilon}_{\theta}=w\cdot\epsilon_{\theta}(z_{t},t,\phi(f_{V}))+(1-w) \cdot\epsilon_{\theta}(z_{t},t,\varnothing).$ (2) $$

In text-based generation, $\varnothing$ is commonly defined as $\phi(\text{" "})$ to represent the null condition. In TTA tasks, we use a zero embedding to serve as the $\phi(NULL)$, representing the null visual condition.

### 3.4 Audio VAE and Vocoder

The audio source is firstly conveyed to the mel-spectrogram using Short-Time Fourier Transform (STFT), and then to the latent representation $z$ with a pre-trained audio VAE. We use the pre-trained VAE from AudioLDM (Liu et al., ). In the reverse process, the audio is recovered from the latent $z$ using the Hifi-GAN vocoder (Kong et al., ).

## 4 Experimental Setup

### 4.1 Dataset

Our primary experiments are conducted on VGGSound Chen et al. (), a dataset comprising over 550 hours of videos with acoustic visual-audio event pairs. We train our models on 200k videos and evaluate them on 3k videos. For certain ablations, we either construct an augmented test set based on the original dataset by randomly cutting and combining segments, or import large unlabelled audio or video corpora for pre-training purposes. More details regarding the dataset preparation, training, and evaluation procedures can be found in section 5.4 and section 5.5.

### 4.2 Objective Evaluation

We utilize commonly used quantitative metrics for the objective evaluation. For evaluating semantic consistency, Fréchet distance (FD), Fréchet audio distance (FAD), Inception Score (IS), Kullback–Leibler (KL) measure the semantic similarity of the generated audios with the ground-truth target in distribution, as well as the generation diversity and quality of the audios. Contrastive language-audio pretraining (CLAP) (Wu* et al., ) score calculates how well the generated audio aligns with the textual description of the audio. For evaluating temporal alignment, AV-Align (Yariv et al., ) assesses the alignment of generated audio with the input video. CAVP (Luo et al., ) evaluates how well the audio aligns with the visual input at both semantic and temporal levels. Besides, Prompting Audio-Language Models (PAM) (Deshmukh et al., ) assesses the quality of generated audio.

### 4.3 Subjective Evaluation

We include a subjective evaluation of our models, assessing overall quality, audio quality, video-audio semantic alignment, and video-audio temporal alignment using a scale of 1 to 100. In the subjective evaluation, we combine the generated audio with the original video pieces, and show the combined video to the human participants without telling them the concrete test cases. Detailed definition of these metrics can be found in appendix B.

## 5 Experiments

### 5.1 Basic Configs

Our LDM structure is configured with a cross-attention dimension of 2048 and features 8 input and output channels. We train our models with 300 warmup steps, a learning rate of 6e-5, and a batch size of 128. All models are trained for a total of 120 epochs. All the models are trained on 8 Nvidia A100 cards. During the inference steps, we set the denoise steps to 300, and the number of samples per audio 1. We utilize classifier-free guidance with a guidance scale of 3.

### 5.2 Initial Results

We first compare our foundational model with other existing VTA baselines to show its capability in audio generation, including IM2WAV (Sheffer & Adi, ), Diff-Foley (Luo et al., ), FoleyCrafter (Zhang et al., ), Seeing&Hearing (Xing et al., ) and T2AV (Mo et al., ). These compared baselines primarily utilize the diffusion framework. Although there are additional VTA baselines available, we consider these to be representative and indicative of SOTA performance. We also list the vision encoders these baselines use for reference. For certain benchmarks, we omit their alignment metrics, as they are tailored to produce shorter audio segments, which does not align with competitive standards. See appendix A for more details about these baselines.

**Table 1: Comparison with other baselines.**
|  | VE | FAD $\downarrow$ | IS $\uparrow$ | FD $\downarrow$ | KL $\downarrow$ | PAM $\uparrow$ | CLAP $\uparrow$ | CAVP $\uparrow$ | AV-Align $\uparrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| GT | - | - | - | - | 0.316 | 0.492 | 0.813 | 0.237 |  |
| IM2WAV | CLIP | 6.32 | 7.46 | 42.13 | 2.38 | 0.182 | 0.306 | 0.781 | 0.192 |
| Diff-Foley | CAVP | 7.32 | 9.62 | 41.09 | 6.03 | 0.226 | 0.441 | 0.802 | 0.187 |
| FoleyCrafter | CLIP | 4.44 | 9.44 | 27.00 | 4.57 | 0.307 | 0.179 | 0.762 | 0.239 |
| Seeing&Hearing | ImageBind | 7.32 | 5.83 | 32.92 | 2.62 | - | - | - | - |
| T2AV | VA-CLAP | 4.05 | 8.02 | 33.29 | 2.12 | - | - | - | - |
| VTA-LDM (Ours) | CLIP4Clip | 2.05 | 10.10 | 25.50 | 3.81 | 0.245 | 0.452 | 0.800 | 0.243 |

Table 1 presents the results of our foundational model compared to existing VTA baselines with different vision encoders (VE), demonstrating its capability in audio generation(^1^11For Seeing&Hearing and T2AV, as the codes are not publicly released, we adopt the metrics from their original papers.). We observe that our model is competitive with all of the baselines across the metrics, showcasing its superiority in generating high-quality, diverse, and temporally-aligned audios. Specifically, our model achieves a lower FAD and FD compared to the IM2WAV and Diff-Foley baseline, indicating better distribution similarity between generated and ground truth audios. Regarding CLAP, CAVP, and AV-Align, our model exhibits superior performance compared to the baselines, emphasizing its ability to generate semantically-related and temporally-aligned audios.

### 5.3 Experiments on Vision Encoders

**Table 2: Ablation on vision encoders.**
|  | FAD $\downarrow$ | IS $\uparrow$ | FD $\downarrow$ | KL $\downarrow$ | PAM $\uparrow$ | CLAP $\uparrow$ | CAVP $\uparrow$ | AV-Align $\uparrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Clip4Clip(VTA-LDM) | 2.05 | 10.10 | 25.50 | 3.81 | 0.245 | 0.452 | 0.800 | 0.225 |
| ImageBind | 8.40 | 6.85 | 42.18 | 8.41 | 0.166 | 0.186 | 0.791 | 0.189 |
| LanguageBind | 2.94 | 9.03 | 28.65 | 4.76 | 0.201 | 0.338 | 0.798 | 0.200 |
| ViViT | 15.99 | 3.88 | 58.14 | 7.33 | 0.288 | 0.223 | 0.766 | 0.189 |
| V-JPEA | 14.28 | 4.27 | 60.04 | 11.89 | 0.142 | 0.080 | 0.786 | 0.186 |
| CAVP | 4.27 | 4.83 | 56.77 | 3.88 | 0.192 | 0.137 | 0.787 | 0.220 |

We begin by investigating how different vision encoders may influence the VTA results. Our study includes popular vision encoders such as Clip4Clip (Luo et al., ), Imagebind (Girdhar et al., ), LanguageBind (Zhu et al., ), V-JEPA (Bardes et al., ), ViViT (Arnab et al., ), and CAVP (Luo et al., ). These vision encoders can primarily be categorized into two groups: 1) image encoders trained with multimodal alignment to enhance semantic understanding (e.g., Clip4Clip and Imagebind), and 2) video feature encoders designed to extract semantic and temporal information from videos. Details can be found in appendix C.

We show the results in table 2. The experiments highlight the influence of different vision encoders on the video-to-audio generation task. The choice of vision encoder plays a critical role in capturing the semantic and temporal information from the input videos, which directly impacts the quality, diversity, and alignment of the generated audio. From the results, it is evident that Clip4Clip, which is based on the pre-trained model Clip, outperforms other methods in generating high-quality and diverse semantically-related audio content. On the other hand, methods like ViViT and V-JPEA, which utilize different vision encoders, show comparatively lower performance in most metrics. While in these cases, CAVP achieves better temporal alignment as shown by the AV-Align score and the CAVP score.

Figure: Figure 2: The saliency map of our model’s interest upon the visual input. We illustrate that VTA-LDM has the ability to learn and concentrate on potential objects capable of producing sound. Furthermore, the model is designed to focus on various sections of the frame across different time intervals, although the attention is calculated based on the final audio latent representation only once.
Refer to caption: x2.png

To further demonstrate how the model’s generation part engagements with the input video, we compute the saliency map of the input visual feature based on the generated audio content, aiming to empirically show how VTA-LDM processes the input video frames. As illustrated in fig. 2, our model focuses on different potential video regions that could potentially be the source of the audio. The model can engage in an attention shuffle with the frames as they progress, indicating its ability to capture temporal information between the frames, despite using the pure semantic vision encoder CLIP. For a safe conclusion, we note that a simple temporal combination of semantic vision features of the video frames may be sufficient to capture the features from the video in VTA tasks, eliminating the need for re-training a video feature encoder.

### 5.4 Experiments on Auxiliary Embeddings

Auxiliary embeddings serve as additional information beyond visual features, potentially enhancing video understanding and anchoring audio events to improve the generation process. We explore the integration of extra information from various modalities, including textual descriptions that encapsulate the overall video content, position embeddings that indicate the sequence of frames, and optical flow that captures pixel-level changes between frames within the video. Details can be found in appendix D.

**Table 3: Ablation on auxiliary embeddings.**
|  | FAD $\downarrow$ | IS $\uparrow$ | FD $\downarrow$ | KL $\downarrow$ | PAM $\uparrow$ | CLAP $\uparrow$ | CAVP $\uparrow$ | AV-Align $\uparrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Text-only | 1.98 | 10.02 | 21.25 | 2.39 | 0.278 | 0.486 | 0.764 | 0.176 |
| Vision-only(VTA-LDM) | 2.05 | 10.10 | 25.50 | 3.81 | 0.245 | 0.452 | 0.800 | 0.225 |
| Vision+Text | 2.09 | 10.61 | 21.13 | 2.47 | 0.284 | 0.486 | 0.802 | 0.228 |
| Vision+PE | 2.36 | 8.16 | 29.71 | 4.88 | 0.250 | 0.332 | 0.801 | 0.223 |
| Vision+Text+PE | 2.99 | 12.71 | 25.98 | 2.76 | 0.280 | 0.476 | 0.794 | 0.189 |
| Vision+Optical Flow | 3.02 | 9.88 | 28.32 | 3.01 | 0.230 | 0.473 | 0.792 | 0.224 |

Results in table 3 show that auxiliary embeddings, such as extra text embeddings and extra position embeddings, provide additional information that can enhance the model’s understanding of the input data and improve the quality of generated audios. Adding extra text embeddings improves the Inception Score (IS) from 10.10 to 10.61 and reduces the FAD, FD, and KL, indicating that the additional semantic information from the text can enhance the quality and diversity of the generated audios, while adding extra position embeddings (PE) appears to have a mixed effect. Combining both text and position embeddings yields the highest IS score of 12.71 but also increases the FAD and reduces the AV-Align score. This suggests that while the combined embeddings can enhance the diversity of the generated audios, they might not necessarily improve the alignment with the ground truth audios.

Figure: Figure 3: A Comparison Between Models Without Additional Text Embedding. The left saliency maps encode the text embedding, while the right ones do not. We demonstrate that extra text embeddings can aid the model in gaining a deeper understanding of the visual content.
Refer to caption: x3.png

We observe that additional embeddings, particularly extra text embeddings, can not only assist the model in comprehending the input video but also concentrate on the actual object producing the audio, thereby improving audio-visual alignment, as illustrated in fig. 3. Visual inputs are more complex and chaotic, making it challenging to discern the true source of the audio. For instance, (on the top left) a woman speaks without moving her mouth, but a non-fine-grained vision encoder cannot recognize this. The extra textual input can help identify the real focus, which is the ”guitar.”

### 5.5 Experiments on Data Augmentation

**Table 4: Ablation on data augmentation.**
|  | FAD $\downarrow$ | IS $\uparrow$ | FD $\downarrow$ | KL $\downarrow$ | PAM $\uparrow$ | CLAP $\uparrow$ | CAVP $\uparrow$ | AV-Align $\uparrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| None(VTA-LDM) | 2.05 | 10.10 | 25.50 | 3.81 | 0.245 | 0.452 | 0.800 | 0.225 |
| Data Alignment Filter | 3.85 | 8.19 | 33.43 | 0.95 | 0.314 | 0.484 | 0.802 | 0.281 |
| Concat Augment | 2.47 | 10.82 | 25.09 | 3.59 | 0.236 | 0.393 | 0.798 | 0.248 |
| Pretrain-Video | 2.93 | 9.90 | 32.10 | 1.28 | 0.292 | 0.267 | 0.769 | 0.262 |
| Pretrain-Audio | 2.48 | 12.23 | 23.61 | 1.38 | 0.232 | 0.469 | 0.798 | 0.250 |

The data used in training is also crucial. We explore several approaches from different perspectives. Initially, we filter the training set using video-audio alignment labels to obtain high-quality video-audio pairs. We use a CLAP (Wu* et al., ) model to help select audio-video pairs with similar semantics based on extra textual labels (with $score>0.3$). We also use the AV-Align score to filter unmatched video-audio pairs (with $score>0.2$). Furthermore, we utilize data augmentation techniques to enhance the diversity and complexity of the training set. We randomly combine video and audio segments to construct a diverse and multi-content set of training data points. Additionally, we leverage extra training sets for pretraining and perform fine-tuning on the foundational model. These approaches are separately studied to illustrate their influence on the final generation results. More details can be found in appendix E.

The influence of different data augmentation methods on the performance of the VTA generation model can be observed in table 4. We compare the base model without augmentation to models utilizing various data augmentation techniques. Data Clean leads to the most significant performance improvement. Removing noisy or irrelevant video-audio samples results in improvements mainly in audio quality and video-audio alignment. The drop in generation quality can be attributed to the decrease in the number of training instances. Concat Augment leads to an improvement in IS, suggesting that this technique can enhance the diversity of the generated audio content, as well as a significant improvement in AV-Align, indicating better video-audio alignment. However, the distance metrics, such as FAD and KL, show a slight decrease with these data augmentation methods. It is worth noting that while data augmentations within the data corpus can improve diversity and alignment, there may be a trade-off in terms of audio content itself. Pretraining with extra data is a natural idea that can help address this issue. The results show that pretraining on extra unlabelled video and audio data can help improve some generation metrics. We believe an appropriate combination of these augmentation methods can enhance the overall performance of the VTA task.

## 6 Further Experiments & Discussion

### 6.1 Subjective Evaluation

**Table 5: Subjective evaluation results.**
|  | Audio Quality $\uparrow$ | Semantic Alignment $\uparrow$ | Temporal Alignment $\uparrow$ | Overall Quality $\uparrow$ |
| --- | --- | --- | --- | --- |
| GT | 91.06$\pm$8.87 | 91.14$\pm$12.54 | 89.66$\pm$14.04 | 91.06$\pm$10.62 |
| Diff-Foley | 48.45$\pm$11.44 | 47.03$\pm$28.45 | 57.17$\pm$28.20 | 46.26$\pm$19.72 |
| IM2WAV | 75.01$\pm$16.77 | 58.35$\pm$28.94 | 48.33$\pm$20.85 | 54.45$\pm$21.59 |
| VTA-LDM | 72.52$\pm$20.71 | 64.92$\pm$24.51 | 43.50$\pm$25.52 | 49.04$\pm$22.23 |
| VTA-LDM+Text | 83.30$\pm$11.45 | 69.45$\pm$22.99 | 59.97$\pm$26.31 | 66.12$\pm$21.57 |
| VTA-LDM+Text+Concat | 86.46$\pm$11.48 | 83.59$\pm$16.69 | 79.32$\pm$17.64 | 82.06$\pm$15.52 |

Figure: Figure 4: Demos of the VTA generation. Given the silent video, our model generates semantically-related and temporally-aligned audios that accurately correspond to the visual events.
Refer to caption: extracted/5930304/figures/vta-demo.png

Figure: Figure 5: Demos of the VTA generation on open-domain videos. Videos are collected from YouTube or generated by OpenAI Sora (Brooks et al., ). Although some test data points may exhibit styles distinct from those in the training dataset, our model concentrates on the semantic comprehension of video content and possesses a certain degree of out-of-domain generalization capability.
Refer to caption: x4.png

We selected several ablation models with relatively better performance for the subjective evaluation to compare against the ground truth and other baselines. In each group, we randomly selected 20 video-audio pairs. The results are presented in Table 5.

In general, we observe that the subjective evaluations from our human participants align with the objective metrics, where our framework outperforms the other baselines. However, we also note that the performance still has room for improvement to reach the level of natural and realistic audio. Upon closer examination of the individual metrics, we observe that the VTA-LDM+Text+Concat model yields the best results among the ablation models, and also surpasses other baselines, indicating the effectiveness of combining auxiliary embeddings and data augmentation techniques. The improvements in semantic and temporal alignment, in particular, highlight the importance of incorporating these elements in the video-to-audio training paradigm.

We also note that almost all baselines exhibit a significant variance. Upon further examination of the generated cases, we find that the models are sensitive to certain factors, such as the complexity of the video scene, the presence of multiple audio sources. From any level, there is still a noticeable gap between the performance of existing VTA models and the ground truth.

### 6.2 Overall discussion

We show a branch of TTA demos in fig. 4. Based on the quantitative results above, we can safely conclude that our vanilla structure has demonstrated its capability to effectively address the semantic alignment of TTA tasks. The vanilla structure’s design enables it to capture and translate the semantic information from the text prompts into the generated audio content, resulting in high-quality, semantically-aligned audio outputs.

However, while the vanilla structure exhibits the potential to address the temporal alignment of TTA tasks, it only partially solves this problem. Temporal alignment, which involves aligning the generated audio events with the corresponding visual events in the video, is a more complex issue that requires additional considerations. We introduced additional modifications, such as position encoding and data augmentation, to enhance the overall performance. Position encoding helps the model better understand the temporal order of events, improving the temporal alignment of the generated audio content. Data augmentation, on the other hand, enhances the model’s generalizability by exposing it to a wider variety of complex data scenarios.
While these modifications have generally led to improvements in both semantic and temporal alignment, they have also occasionally resulted in negative feedback, as shown in table 3 and table 4.

Figure 4 shows that our model generally performs well in temporally generating corresponding audios in several complex scenarios. However, there are instances where the model slightly miscatch the change of the scene. From our point of view, several key problems cannot be solved by the current technique, which may be ignored in the previous study: 1) the presence of an object in a video doesn’t necessarily equate to it producing a sound. Some objects can produce different sounds depending on the context or the action being performed, and not all objects in a video make a sound. Models often fail to recognize these ’silent’ objects in a video. Nevertheless, our model shows robust generation capabilities in open-domain tasks, generating natural and realistic audios from silent YouTube or even AI-generated videos, as shown in fig. 5.

## 7 Conclusion

In this paper, we delve into the VTA task, which aims to generate semantically-related and temporally-aligned audios given silent video pieces. Leveraging the diffusion-based backbone of the VTA model, we demonstrate the effectiveness of our VTA-LDM framework and conduct various ablations in the model design and training process. We also dive into several key aspects during the VTA generation, aiming at giving the community more insights about the model design and training process. Specifically, we concentrate on the vision encoder, auxiliary embeddings, and data augmentation techniques employed in the training process, and propose several significant suggestions that have been quantitatively validated through our experiments. A combination of these approaches can lead to a more powerful VTA backbone. We foresee the evolution of more realistic and natural video-audio generation models based on the insights gained from this study.

Limitations & future work The models presented in this paper are trained on a limited dataset, VGGSound, which primarily contains audios of single audio event data. Although we employ data augmentations and utilize a portion of the filtered YouTube test set, there is room for improvement. As part of our future work, we plan to build a more extensive, more diverse, and real-world-like high-quality dataset.

Social impact The capability to generate realistic audio from silent visual input can significantly enhance the accessibility of high-quality video-audio content, benefiting fields such as AI-generated content (AIGC) and virtual reality simulations. However, potential ethical concerns arise, including the possibility of misuse in creating deep fake videos or misleading audio content.

## Appendix A Baselines

We compare with these open-sourced VTA baselines:

- 1.
IM2WAV (Sheffer & Adi, ) is an open-domain audio
generation system which is based on the image or the image sequence. The model uses a language model to generate low-level audio representation. We use the pre-trained checkpoint with the default parameters as the baseline.
- 2.
Diff-Foley (Luo et al., ) uses a LDM based on the features extracted by CAVP, an encoder that is contrastively pre-trained to learn temporally and semantically aligned audio-visual features. We use the pre-trained Diff-Foley as our baseline.
- 3.
FoleyCrafter (Zhang et al., ) imports semantic and temporal blocks for precise audio-video synchronization, and supports the use of text descriptions to facilitate controllable and diverse TTA generation.
- 4.
Seeing&Hearing (Xing et al., ) is built on a multimodality latent aligner with the pre-trained ImageBind model. It also uses a LDM as the generation framework.
- 5.
T2AV (Mo et al., ) leverages visual-aligned text embeddings as its conditional foundation in diffusion-based audio generation. Note that, compared with other baselines, T2AV generation is more based on the textual description. The model leverages a pre-trained video-audio CLAP(VA-CLAP) as the vision encoder.

## Appendix B Subjection Evaluation

The subjective evaluation aims to gauge the performance of our models from a human perspective, providing insights into the perceived quality and alignment of the generated audio and original video content. Participants undergo a training session where they are introduced to the evaluation objectives, the rating scale, and example demonstrations that illustrate different quality levels. Additionally, they practice rating sample videos to familiarize themselves with the criteria and receive feedback to ensure consistency.

#### Overall Quality

This metric evaluates the general appeal and coherence of the combined video, considering both audio and visual components. It contains how well the audio and video elements fit together without any jarring or inconsistent moments.

#### Audio Quality

Focusing solely on the generated audio, this metric assesses factors such as clarity, fidelity, and naturalness, like how clear and understandable the audio is, free from distortion or muddiness, and how closely the generated audio resembles human speech or natural sounds in terms of intonation, rhythm, and expression.

#### Video-Audio Semantic Alignment

This metric measures how well the audio semantically matches the visual content, ensuring that the sounds correspond appropriately to the actions and scenes depicted in the video, like how relevant the audio is to the visual context, with sounds matching the actions and scenes on screen.

#### Video-Audio Temporal Alignment

The evaluation centers on the synchronization between the audio and video streams, determining how accurately the timing of audio events aligns with visual events, like the precision with which sounds occur in tandem with corresponding visual actions (e.g., a door slamming in sync with the sound of the slam).

## Appendix C Experiments on Vision Encoders

Based on the proposed generation framework, we start by trying ablation results on different vision encoders:

- 1.
Clip4Clip (Luo et al., ) foucuses on video-text retrieval based on the pretrained Contrastive Language-Image Pre-training (CLIP) (Radford et al., ) model. Experiments have shown that CLIP can serve as the backbone to extract knowledge from frame-level video input. We leverage a similar linear projection to map the frame-level video features extracted by CLIP to get the generation condition.
- 2.
Imagebind (Girdhar et al., ) is pre-trained on different modalities to learn joint embeddings, including images, text, and audio. All the modalities are bound with the image-paired data. We leverage Imagebind to get the image embeddings of the extracted frames extracted from the input video.
- 3.
LanguageBind (Zhu et al., ) is trained similarly with Imagebind but takes the language as the bind across different modalities. As the language modality is well-explored and proved influential as conditions in the previous TTA works, we would like to see whether vision embeddings extracted from LanguageBind can serve as a better condition.
- 4.
V-JEPA (Bardes et al., ) is considered as an extension of the I-JEPA (Assran et al., ) to video based on self-supervised learning. Improving from I-JEPA which learns semantic image features, V-JEPA deals with self-supervised learning of video representations appropriate for video understanding in a spatio-temporal way.
- 5.
ViViT (Arnab et al., ) a pure-transformer based models. Although the model was originally designed for video classification, we would like to check whether the spatio-temporal tokens learned from the input video can help guide the generation of audio. We use the pre-trained ViViT trained on the kinetics400 dataset as the encoder.
- 6.
CAVP (Luo et al., ) is trained with two different objects, semantic contrast loss and temporal contrast loss, to improve audio-video features’ semantic and temporal alignment. CAVP was first leveraged in the DIFF-FOLEY model to synthesize synchronized video-conditioned content. We use the pre-trained CAVP (also pre-trained on the VGGSound dataset) as our vision encoder.

## Appendix D Experiments on Auxiliary Embeddings

We are also interested in exploring whether auxiliary embeddings, beyond visual features, could enhance the generation process. Numerous studies have demonstrated that additional information can improve generation results in various ways, whether broadly or specifically. We aim to investigate this phenomenon through several auxiliary embeddings:

- 1.
Text Information: Additional textual labels can provide valuable context and extra semantic details that may not be immediately discernible from visual features. Moreover, text information can assist in filtering out extraneous information in the video. For instance, the presence of a gun in a video does not necessarily imply that the corresponding audio will be generated. In our experiment, we utilize CLIP to obtain text embeddings and concatenate these with the video embeddings to serve as the condition.
- 2.
Position Embedding: Indeed, Position Embedding is critical as it imparts a sense of temporal order or sequence, which is essential in audio generation, particularly when the vision encoders are primarily focused on the semantics of the audio. Intuitively, Position Embedding assists the model in understanding the event sequence, thereby enabling it to generate coherent audio. We utilize sinusoidal positional embedding, akin to the method used in the Transformer model (Vaswani et al., ), as it facilitates effortless attention to relative positions.
- 3.
Optical Flow: Optical Flow offers valuable insights into the motion and dynamics present in a video sequence. Previous study (Fedorishin et al., ) has utilized this information to assist in localizing sound sources within videos. In our approach, we employ optical flow video embeddings as an additional condition for generation. Similar to the previous embeddings, these embeddings are concatenated with the original video embeddings, enriching the input representation and potentially enhancing the audio generation process.

## Appendix E Experiments on Data Augmentation

The data quality of the training set is undeniably crucial for a model’s performance, particularly when training large generative models. We explore data augmentation from three different perspectives:

- 1.
Data Clean: Data clean ensures that the input data is accurate, consistent, and free from errors or anomalies. We use a CLAP (Wu* et al., ) model to help select audio-video pairs with similar semantics based on extra textual labels (with $score>0.3$). We also use the AV-Align score to filter unmatched video-audio pairs (with $score>0.2$). For the VGGSound dataset, we filter out about 100k high-quality video-audio pairs.
- 2.
Concat Augmnent The original VGGSound dataset primarily contains audio clips with single audio events. While this makes the dataset a clean, simple test set for audio event generation, it does not evaluate the model’s ability to handle temporal information in complex videos. To simulate complex generation tasks with various audio events during training, we randomly concatenate two videos with different audio events. We also propose a test set to assess our models.
- 3.
Pretrain We also try to leverage the power of pretraining. We pretrain our model on two different data corpus seperately: a large video-audio corpus consisting of 10k hours of content from YouTube, and a large audio corpus mainly from WavCaps (Mei et al., ) and Youtube, amounting to approximately 150k hours of audio with paired captions. The YouTube corpus is processed in the same manner as the VGGSound video data. We filter out talking and music content, using only audio event cases for training. For video-audio pretraining, we leverage video-to-audio supervised training. For audio pretraining, we perform audio self-supervised training. Subsequently, we perform full parameter finetuning of our model on the original training set.

## Appendix F More Demos

### F.1 Sailency Map

Figure: Figure 6: More demos of the sailency map. We show that the model focuses on different components in different frames of the given video after training, although the vision encoder parameters are completely freezed during the training time.
Refer to caption: x5.png

### F.2 Audio Generation

Figure: Figure 7: More demos of the VTA generation on open-domain videos. Refer to the supp materials for more video demos and the inference code.
Refer to caption: x6.png

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: 1.	Is the LDM learned from scratch?
2.	The CLIP4CLIP feature is not clear to me. Does it use the spatial-temporal feature, or is it just a mean pooled feature vector, as indicated by the original paper?
3.	What is the position embedding in L338? Is it the video position embedding or text position embedding? 
4.	I’d like to see the explanation on why the attention on video is more focused by adding text, as shown in Fig.3. I think this salience map is the UNet activation as query and the video patch feature as key.
5.	How to remove those videos where the background music is not that correlated with the video content?
soundness: 3
strengths: 1.	This paper proposes a baseline of the LDM model that surpasses the other comparison methods in some metrics. 
2.	It performed various ablations on video encoder selection, data augmentation, and text embedding, adding up to this paper's empirical value.
summary: This paper proposes a video-to-audio model by investigating vision encoders, auxiliary embeddings, and data augmentation techniques. It shows an SOTA performance. 
It uses the CLIP4CLIP as a video encoder, LDM, to generate the mel-spectrogram space.
It uses VGGSound 200k videos for training. It also filters out the video-audio pairs with low similarity.
weaknesses: 1.	It shows that the text embedding would help the model generate the audio; I think it makes sense, but I’d like to know how details of the caption are needed. Does dense video caption help, or just a succinct video caption is enough? 
2.	Minor: Why do Experiment Setup and Experiments in separate sections? I think it’s common to pub experimental setup as a subsection under the experiment section.
3.	In L383, the data augmentation is to randomly combine video and audio segments. But does this reduce the alignment between video and audio?
4.	It seems the author did many kinds of data preprocessing but only evaluated them independently and expected the mixture of them to work the best. However, I think it should be within the scope of this paper to give a final combined setting of those data augmentation, which makes me feel the unreadiness of this paper.
5.	I found this paper a baseline with empirical ablation, but it is not clear where the novelty lands.

---

confidence: 5
contribution: 1
initial_rating: 3
presentation: 4
questions: Following my weakness mentioned above, I would like to see answers to the following questions:

1. The proposed method's performance, and comparison, on Landscapes (Compare to Seeing&Hearing) and AVSync15(Compare to FoleyCrafter).

2. The author's explanation on strange training/testing splits of VGGSound, and inconsistent data in Table 1 & Table 2.

3. A conclusion on, under fair comparison, whether the proposed model is truly SOTA or not, and provide more concrete  comparison between the proposed model and others, as well as some analysis on why (or why not) the proposed model is SOTA.

4. The 3 questions I've asked in Last point in weakness.
soundness: 2
strengths: 1. Lots of studies on the design choices.
While many components of the model inherits previous pretrained frozen models (which I don't think is a weakness), the author has experimented with many versions of vision encoders (6 in the paper Table 2.).

2. Good paper writing.
The paper is well written. Despite a few missing details, the overall paper is easy to follow.

3. Lot's of evaluation metrics.
The paper has used lots of evaluate metrics, which help in assessing the overall model in semantics alignment and synchronization.
summary: The paper proposed a Video-to-Audio generation framework. The model consists of a pretrained audio VAE and vocoder, a latent-diffusion-model, and CLIP-based vision encoder. The paper studied different design choices, such as using different vision encoders, input different conditioning features, and different data augmentation methods during training.
weaknesses: 1. Lack of evaluation dataset and synchronization metrics
VGGSound is in fact a noisy dataset to measure both: on one hand, it contains overwhelming audio noises, and many video audio pairs are not semantically aligned; on the other hand, the video-audio synchronization is even poor due to camera shaking, out of scene sound sources, etc. Previous papers on VTA has evaluated on some other datasets, which can be simpler but much cleaner, e.g., evaluate semantics alignment on Landscapes dataset (Seeing&Hearing), or evaluate both semantics alignment and synchronization on AVSync15 (FoleyCrafter). Without evaluation on these datasets, the single-dataset (and also noisy dataset) evaluation results is pretty hard to convince the community.

The author explained in Sec. 5.2 to "omit alignment metrics, as they are tailored to produce shorter audio segments, which does not align with competitive standards". This reason is weak. Suppose the other datasets/metrics do have shorter audio-video segments, the author can simply extend the input video with black/white/the last frame, then after getting the audio, only retrieve the first several seconds' content. In fact, there are lots of videos in VGGSound that have frame sudden changes (some frames in one video are even not related to each other), therefore, the method I mentioned to deal with shorter video-audio segments is absolutely feasible.

2. Inconsistent / strange evaluation.

On VGGSound, the author mentioned in Sec. 4.1 that, on VGGSound, they are using 200k video for training and 3k for testing. Even without considering broken youtube links, VGGSound's original annotation file only contains 183,971 training videos and 15496 testing videos, summing up to no more than 200k. How come the author can use 203k VGGSound videos? Do the training and testing splits overlapping with each other?

Also, the evaluation metrics are inconsistent. For the same model and CLIP4CLIP encoder, the AV-Align metrics is reported as 0.243 in Table 1 but 0.225 in Table 2, while other metrics being the same. If this was a typo, I would consider it as very serious mistake.

3. Weaker performance compared to SOTA.
When comparing with other methods in Table 1, the author used CLIP4CLIP as the encoder. However, the proposed model, once equipped with ImageBind encoder (Note that ImageBind vision encoder uses frozen CLIP image encoder, which makes it the same as CLIP encoder in Table 1), the performance becomes much worse than some other methods. E.g., AV-Align is 0.189 compared to FoleyCrafter's 0.239, CLAP is 0.186 compared to IM2WAV's 0.306, PAM is 0.166, the worst among all compared models. That means, under fair comparison, the paper cannot achieve SOTA performance, as opposed to what is claimed.

Besides, even the proposed model is not stronger than previous methods, there should always be a more detailed comparison between the proposed framework and previous works. E.g., DiffFoley and FoleyCrafter also used LDM for generation, what is the difference between the proposed LDM and theirs?

4. Some missing but important details.

(1) In Sec. 3.3, when UNet attending the vision features, does each audio feature only see vision features within its corresponding time window? Or, each audio features is attended to all vision features, regardless its time? If it is the former, how exactly did you split the vision tokens to achieve time-aware attention? If it is the latter (every audio feature in UNet sees exactly the same vision encoded feature, regardless of time) and the model can still achieve good synchronization results, then the only explanation should be that, the vision features have already be implicitly encoded with time information (e.g., CLIP4CLIP might already have positional encodings on temporal dimension), which might be the reason why using PE does not help in Table 3? Any more discussions on this?

(2) How did you sample the 3k validation data? Did you first apply data alignment filter to get a clean subset first, then choose from it? Or did you simply random sample from original noisy VGGSound?

(3) There should be formal explanation on each metric you are using, at least in supplementary material. The current rough explanation in Sec. 4.2 is vague. For example, the author mentioned to use CAVP as semantics and synchronization evaluation metric. However, CAVP is only a contrastively trained model in DiffFoley. The original DiffFoley paper used a trained classifier to tell synchronization and semantics alignment instead, and called the metric "Align Acc". Did you use CAVP features just as in CLIPSim for evaluation, or did you use Align Acc? The detail is missing though.

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 2
questions: - See the first point in Weaknesses.
- Is the concatenation-based augmentation in section 5.5 the same one with that used in DiffFoley? If so, it is quite counter-intuitive that that augmentation does not boost the temporal alignment (actually drops it a bit in CAVP score).
soundness: 1
strengths: - The motivation behind this study is significant for a deeper understanding on how we should construct video-to-audio models. Many recent video-to-audio models are constructed by extending audio generation models to make them accept video (and any other auxiliary) conditions. Thus, the choice of vision encoders and auxiliary embeddings should be a particular and important challenge, which has empirically been explored in this study. Rigorous investigation would be expected to significantly benefit future research on video-to-audio technology.

- The baseline framework seems quite simple and appears straightforward to implement. The vision encoders and audio VAEs examined in this paper are all publicly available ones, which would greatly help the reproduction of the reported empirical results.
summary: This study empirically investigates how the performance of video-to-audio models would be affected by three algorithmic designs: vision encoders, additional usage of auxiliary conditions, and data augmentation techniques. The experimental results show that state-of-the-art performance can be achieved by choosing the best option in each design. They also bring several important observations: for example, the additional usage of auxiliary conditions helps the model to focus on the actual object producing the audio, and filtering training data boosts the audio-visual alignment while dropping the overall generation quality.
weaknesses: - Several descriptions in the paper are unclear, making it difficult to follow the authors' arguments.
  - Each vision encoder should have a different resolution (and provide a different size of features), but its details are not given in the paper. I would suggest that the authors provide a table or detailed description of the temporal and spatial resolutions used for each vision encoder, as well as how these were standardized (if at all) for fair comparison.
    - Particulaly, it would be beneficial to include the information related to temporal resolution of the features, because it should heavily affect the model's capability on temporal alignment. For example, in the standard setting, CAVP features would be extracted from 4-fps videos, while ImageBind features are from 1-fps videos.
  - Tables 1 and 4 show the evaluation results by multiple metrics, but it is not clear which metric means what, which makes it hard to properly understand the findings. For example, around l.392, the authors state that audio quality (maybe PAM) has been improved while generation quality (maybe FAD/IS) drops, but it is confusing due to the lack of the explanation on how these two kinds of quality differ.
  - The details of "Pretrain" in section 5.5 are missing. Actually, it may not align with the typical definition of data augmentation, which does not alter the main training objective.
  - The saliency maps shown in Fig. 2 seem interesting, but how to compute this saliency is not described.
  - The term "hidden alignment" in the title does not appear in the main text and is not clearly defined.

- The insights from the empirical results are somewhat limited.
  - In section 5.3, various encoders are examined in the experiments, but it is not clear which aspects of the vision encoder contribute the most to boosting the performance of the video-to-audio model, because they differ from each other in many ways such as resolution, model size, training dataset, pretraining method, performance on downstream tasks, etc. Empirically verifying this should be one of the most important insights for the choice of the vision encoder to be used in future video-to-audio studies. Simply speaking, what property of CLIP4clip contributed most to the performance gain?
  - A similar issue as above also exists in section 5.5. The authors argue that the data filtering leads to the most significant performance improvement, but, as it comprises two filtering processes based on semantic and temporal alignment, it is not clear which filtering contributes more. In addition, it is also not clear why the authors choose CLAP and AV-Align for the filtering process, though there are various other choices examined in section 5.3 (namely, ImageBind, LanguageBind, and CAVP).