Video Object Segmentation-Aware Audio Generation
This paper presents a new way to generate audio for videos that focuses on specific objects rather than the entire scene.
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- 1 Thus, the conditional flow matching objective is derived as follows:.
- 2 It is a metric of objective quality and diversity.
- 3 In addition to objective evaluations, we conduct a subjective user study presented in Table 2 .
- 4 We perform the study for the same models as the objective evaluations.
Introduction
Multimodal audio generation focuses on synthesizing audio, given a conditional video feed, a textual description, or conditions in other modalities. Recent advances in diffusion and conditional flow matching (CFM) models have improved the audio quality of the artificial Foley models .
However, existing models still lack precise user guidance and control.
Although recent models introduce more modalities to guide the audio generation process, they still lack precision.
SAGANet generates matching audio, but MMAudio is unable to focus on the target object conditioned solely on SAGANet generates accurate audio for the target object.
Research Question
Thus, the flow matching objective becomes:. Thus, the conditional flow matching objective is derived as follows:.
Joint attention blocks aim to incorporate information across the modalities.
It is a metric of objective quality and diversity.
Methodology
To improve control in Foley generation, we propose a new audio synthesis task: video object segmentation-aware audio generation. This task focuses on nuanced control, highlighting the model’s ability to generate audio for a specific object in the video rather than for the full scene.
Study Design
To the best of our knowledge, we are the first to utilise visual segmentation information in the audio generation task.
Our contributions can be summarized as follows: i) a new audio synthesis task, namely video object segmentationaware audio generation, ii) a video object segmentationaware control for a state-of-the-art multimodal generative audio model, iii) we show that by training our model with single-source samples it can generate audio for target object in multi-source scenes, and iv).
Future work will therefore focus on building more diverse datasets, and training and evaluating the proposed method on them.
Future work should address the limitations of the proposed method.
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Results & Findings
These models can be utilized in Foley processing, where the goal is to produce a soundtrack for a video. For the artificial Foley models to thrive, e.g., as a video post-processing tool, the end-user has to have high control over the synthesized result.
- These models can be utilized in Foley processing, where the goal is to produce a soundtrack for a video.
- For the artificial Foley models to thrive, e.g., as a video post-processing tool, the end-user has to have high control over the synthesized result.
- In addition to controllability, the model has to produce high-quality samples with great semantic and temporal accuracy.
- It demands significant computational resources and quickly becomes infeasible in academic environments.
- Alternatively, recent works reduce training costs by using lightweight aligners or adapters to condition pretrained textto-audio models on video sequences .
Manual prompting yielded more coherent masks compared to using a grounding model, but limited resources prevented us from manually annotating target objects in the training data.
Practical Applications
This indicates that while the generated audio may be of reasonable perceptual quality, it lacks relevance to the target object.
Video Object Segmentation
This section defines video object segmentation (VOS) and its types, including semi-supervised and unsupervised methods. It discusses recent advancements in VOS models, particularly the SAM2 model, and how these models can be integrated into audio generation tasks.
Frequently Asked Questions
Thus, the conditional flow matching objective is derived as follows:. It is a metric of objective quality and diversity.
To the best of our knowledge, we are the first to utilise visual segmentation information in the audio generation task. Also, another transformer-based approach is to utilize Masked Generative Image Transformer (MaskGIT) schema for the audio generation task .
These models can be utilized in Foley processing, where the goal is to produce a soundtrack for a video. Although our model is trained on videos with a single audio source, it can generalize to scenes with multiple audio sources and generate.
SAGANet generates matching audio, but MMAudio is unable to focus on the target object conditioned solely on video and target instrument label even though the overall audio quality is sufficient, the temporal alignment is missing. Overall audio quality also improves, indicating that.
SAGANet generates matching audio, but MMAudio is unable to focus on the target object conditioned solely on video and target instrument label even though the overall audio quality is sufficient, the temporal alignment is missing. Overall audio quality also improves, indicating that.
This paper presents a new way to generate audio for videos that focuses on specific objects rather than the entire scene.