Comprehensive Benchmark for Text-to-Audible Video Generation

Core Concepts

The core message of this article is to introduce a new task called Text to Audible-Video Generation (TAVG), which requires generating synchronized audio and video content from text descriptions. To support this task, the authors propose a large-scale benchmark dataset called TAVGBench and a baseline model called TAVDiffusion that leverages latent diffusion to jointly generate audio and video.

Abstract

The article introduces a new task called Text to Audible-Video Generation (TAVG), which aims to generate videos with accompanying audio based on text descriptions. This task is an extension of the existing text-to-video generation task, as it requires the model to generate both audio and video content simultaneously. To support research in this field, the authors have developed a comprehensive Text to Audible-Video Generation Benchmark (TAVGBench) dataset, which contains over 1.7 million video clips with a total duration of 11.8 thousand hours. The dataset includes detailed annotations for both the audio and video components, generated using a coarse-to-fine pipeline that combines state-of-the-art models (BLIP2, WavCaps) and the language model ChatGPT. The authors also introduce a new metric called the Audio-Visual Harmoni score (AVHScore) to quantify the alignment between the generated audio and video content. Additionally, they present a baseline model for TAVG called TAVDiffusion, which uses a two-stream latent diffusion architecture to generate audio and video simultaneously. The model employs cross-attention and contrastive learning to achieve alignment between the two modalities. Extensive experiments and evaluations on the TAVGBench dataset demonstrate the effectiveness of the TAVDiffusion model, which outperforms the comparison methods in terms of both conventional metrics (FVD, KVD, CLIPSIM, FAD) and the proposed AVHScore. The authors also showcase the model's zero-shot capabilities by evaluating it on the FAVDBench dataset, which was not used during the training phase. The article highlights the potential applications of the TAVGBench dataset and the TAVDiffusion model in various multimedia domains, such as audible video captioning and multimodal content creation.

Stats

A man is seated in front of a microphone, facing a computer screen. A man is audibly speaking and taking breaths with background noise. The man is audibly speaking and taking breaths, with background noise audible. Seated in front of a microphone and facing a computer screen, he appears to be engaged in recording or broadcasting activities. A truck is driving on a street, with smoke visibly coming out of its tires as it moves. The sound of a car revving its engine loudly and screeching its tires can be heard as it accelerates. A pristine sky with a few white clouds, white snow-capped mountains in the distance and the sound of birds chirping in the background. As music plays in the background, a group of people can be seen chatting and applauding in the video, which captures various scenes and attractions from Disneyland. Visitors are shown enjoying rides, meeting characters, and exploring different themed lands throughout the park. A handsome man wearing a blue coat is playing the guitar in the house, showcasing his skill and rhythm control. He seemed to be attracted by the singing and seemed very intoxicated. The white ambulance is traveling quickly. buildings and parked automobiles line the road. alongside the road are lush trees. sound of an ambulance siren. Screaming in the air a wolf with a white head and a yellowish-brown body lay on the ground. A golden leaf that has fallen to the ground is covered in snow. The wolf kept barking and it was really loud.

Quotes

"A man is seated in front of a microphone, facing a computer screen." "A truck is driving on a street, with smoke visibly coming out of its tires as it moves. The sound of a car revving its engine loudly and screeching its tires can be heard as it accelerates." "Screaming in the air a wolf with a white head and a yellowish-brown body lay on the ground. A golden leaf that has fallen to the ground is covered in snow. The wolf kept barking and it was really loud."

Key Insights Distilled From

TAVGBench: Benchmarking Text to Audible-Video Generation

by Yuxin Mao,Xu... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14381.pdf

Deeper Inquiries

How could the TAVGBench dataset be extended to include more diverse audio-visual content, such as live-action scenes, animations, or virtual environments?

To enhance the diversity of the TAVGBench dataset, several strategies can be implemented: Incorporating Live-Action Scenes: Collaborate with filmmakers or production studios to include professionally shot live-action videos with corresponding audio descriptions. This can add realism and complexity to the dataset, capturing a wider range of human activities and environments. Adding Animated Content: Partner with animators or animation studios to include animated videos with accompanying audio descriptions. This can introduce a different style of content and cater to scenarios that are not feasible in live-action filming. Integrating Virtual Environments: Utilize virtual reality (VR) technology to create immersive virtual environments and scenarios. By capturing audio-visual content within these virtual worlds, the dataset can encompass unique and interactive experiences. Crowdsourcing Contributions: Engage a diverse group of contributors to submit audio-visual content from various sources, including user-generated videos, public domain footage, and creative commons animations. This crowdsourcing approach can significantly expand the dataset's breadth and depth. Curating Specific Themes: Curate specific themes or genres, such as sports, nature, technology, or historical events, to ensure a well-rounded representation of different audio-visual content categories. This targeted approach can enrich the dataset with specialized content. By implementing these strategies, the TAVGBench dataset can evolve into a comprehensive repository of diverse audio-visual content, encompassing a wide range of scenarios, styles, and environments.

How could the TAVDiffusion model be scaled up to handle longer video sequences or more complex audio-visual interactions?

Scaling up the TAVDiffusion model to accommodate longer video sequences or complex audio-visual interactions involves several considerations: Hierarchical Diffusion: Implement a hierarchical diffusion framework that processes video sequences in segments or chunks, allowing the model to handle longer videos efficiently. By hierarchically diffusing latent variables at different levels, the model can manage extended sequences without overwhelming computational resources. Temporal Fusion: Introduce mechanisms for temporal fusion to capture long-range dependencies and interactions across frames in the video. Techniques like temporal convolutions, recurrent neural networks, or transformer architectures can be integrated to enhance the model's ability to understand temporal dynamics in audio-visual data. Parallel Processing: Utilize parallel processing and distributed computing to accelerate the model's inference on longer video sequences. By leveraging multiple GPUs or distributed systems, the model can efficiently process and generate audio-visual content in real-time or near real-time. Memory Optimization: Optimize memory usage and computational efficiency by implementing memory-efficient architectures, such as sparse activations, gradient checkpointing, or memory sharing mechanisms. This can help mitigate the computational burden associated with processing longer videos. Adaptive Sampling: Implement adaptive sampling strategies to focus computational resources on relevant segments of the video that contain critical audio-visual interactions. By dynamically adjusting the sampling rate or attention mechanisms, the model can prioritize processing key segments within longer sequences. By incorporating these strategies, the TAVDiffusion model can effectively scale up to handle longer video sequences and more intricate audio-visual interactions, enabling the generation of high-quality audible videos across diverse content types.

How could the proposed techniques be adapted to enable interactive or user-guided audible video generation, where the user can provide real-time feedback or adjustments to the generated content?

Adapting the proposed techniques for interactive or user-guided audible video generation involves the following steps: Real-time Feedback Mechanism: Implement a real-time feedback loop where users can provide input on the generated content. This feedback can include preferences for audio styles, visual elements, or narrative directions, allowing users to guide the generation process. Interactive Interface: Develop an interactive interface that enables users to interact with the model and make adjustments to the generated audio-visual content. This interface can include sliders, buttons, or text inputs for users to modify parameters like audio volume, visual effects, or scene transitions. User-Driven Prompts: Allow users to input specific prompts or descriptions to influence the generation process. By incorporating user-provided text descriptions or keywords, the model can tailor the output to align with the user's intentions and preferences. Dynamic Content Generation: Enable dynamic content generation based on user interactions, where the model adapts in real-time to user feedback and adjusts the audio-visual output accordingly. This dynamic approach ensures that users have control over the creative process and can shape the content as it unfolds. Collaborative Generation: Facilitate collaborative generation experiences where multiple users can contribute to the creation of audible videos simultaneously. This collaborative approach fosters creativity, engagement, and shared storytelling among users interacting with the model. By integrating these features, the proposed techniques can be tailored to support interactive and user-guided audible video generation, empowering users to actively participate in the content creation process and customize the output according to their preferences and creative vision.