Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interaction
Introduced HVGC framework and BridgeDiT model for text-to-sounding video generation, outperforming existing methods.
Key Findings
Methodology
The study proposes the Hierarchical Visual-Grounded Captioning (HVGC) framework to generate disentangled captions for video and audio, eliminating modal interference. Based on this, the BridgeDiT model, a dual-tower diffusion transformer, is introduced, employing a Dual Cross-Attention mechanism to achieve semantic and temporal synchronization.
Key Results
- On the AVSync15 dataset, the BridgeDiT model outperformed all baselines in metrics like FVD, KVD, and FAD, particularly excelling in audio-video synchronization.
- Experiments on VGGSound-SS and Landscape datasets showed significant improvements in both audio and video quality.
- Ablation studies validated the effectiveness of the CRR caption framework and the Dual Cross-Attention mechanism, highlighting their importance in multimodal interaction.
Significance
This research is significant for both academia and industry, addressing key issues in text-to-sounding video generation, such as modal interference and unclear cross-modal feature interaction mechanisms. By introducing new frameworks and models, it enhances generation quality and synchronization.
Technical Contribution
Technical contributions include the introduction of a new caption generation framework and a dual-tower diffusion transformer architecture, significantly improving text-to-sounding video generation performance. Compared to existing methods, it provides new theoretical guarantees and engineering possibilities.
Novelty
This study is the first to propose the Hierarchical Visual-Grounded Captioning (HVGC) framework and Dual Cross-Attention mechanism, addressing modal interference and synchronization issues in text-to-sounding video generation.
Limitations
- The model may perform poorly in handling complex background sounds, potentially requiring more training data.
- High hardware resource requirements lead to significant training and inference costs.
Future Work
Future research directions include optimizing the model's computational efficiency, exploring more application scenarios, and further improving generation quality and synchronization.
AI Executive Summary
Text-to-sounding video generation (T2SV) is a complex yet promising task aimed at generating videos with synchronized audio from text conditions. Despite progress in joint audio-video training, two critical challenges remain: modal interference and unclear cross-modal feature interaction mechanisms. To address these issues, the study proposes the Hierarchical Visual-Grounded Captioning (HVGC) framework to generate disentangled captions for video and audio, eliminating modal interference. Based on this framework, the BridgeDiT model, a dual-tower diffusion transformer, is introduced, employing a Dual Cross-Attention mechanism to achieve semantic and temporal synchronization. Experimental results demonstrate that this method achieves state-of-the-art results on multiple benchmark datasets, particularly excelling in audio-video synchronization. Future research directions include optimizing the model's computational efficiency, exploring more application scenarios, and further improving generation quality and synchronization.
Deep Analysis
Background
Text-to-sounding video generation (T2SV) is an emerging research field aimed at generating videos with synchronized audio from text. Recent years have seen rapid progress in text-to-video (T2V) and text-to-audio (T2A) generation technologies, but independently generating video and audio often fails to achieve temporal synchronization. Joint generation methods are gradually becoming mainstream but still face issues of modal interference and unclear interaction mechanisms.
Core Problem
The core problem of the T2SV task is how to generate synchronized audio and video from text. Existing methods often use shared captions, leading to modal interference. Additionally, the optimal interaction mechanism for cross-modal features remains unclear, affecting generation quality and synchronization.
Innovation
The study proposes the Hierarchical Visual-Grounded Captioning (HVGC) framework to generate disentangled captions for video and audio, eliminating modal interference. It introduces the BridgeDiT model, a dual-tower diffusion transformer employing a Dual Cross-Attention mechanism to achieve semantic and temporal synchronization.
Methodology
- �� Hierarchical Visual-Grounded Captioning (HVGC): Generates disentangled captions for video and audio.
- �� BridgeDiT Model: A dual-tower diffusion transformer employing Dual Cross-Attention.
- �� Dual Cross-Attention Mechanism: Achieves semantic and temporal synchronization.
Experiments
Experiments were conducted on AVSync15, VGGSound-SS, and Landscape datasets, using metrics like FVD, KVD, and FAD to evaluate generation quality and synchronization. Ablation studies validated the effectiveness of the CRR caption framework and the Dual Cross-Attention mechanism.
Results
The BridgeDiT model achieved state-of-the-art results on multiple benchmark datasets, particularly excelling in audio-video synchronization. Ablation studies showed the importance of the CRR caption framework and the Dual Cross-Attention mechanism in multimodal interaction.
Applications
This technology can be applied in fields such as film production, game development, and virtual reality, enhancing the quality and synchronization of audio-visual content.
Limitations & Outlook
The model may perform poorly in handling complex background sounds and has high hardware resource requirements, leading to significant training and inference costs. Future research directions include optimizing the model's computational efficiency and exploring more application scenarios.
Plain Language Accessible to non-experts
Imagine you're in a kitchen, and the kitchen is this model. You have two chefs, one responsible for cooking (video) and the other for seasoning (audio). They need to work simultaneously and coordinate with each other to make a delicious dish. This model acts like a coordinator, ensuring the two chefs do the right things at the right time without interfering with each other. With the new method, this model can better coordinate the chefs' work, ensuring the dish's taste and appearance are perfectly presented.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to control both the character's actions and the background music. This new technology is like a super helper, helping you perfectly sync the character's actions with the music. For example, when the character jumps, the music automatically becomes exciting. When you need to focus on defeating enemies, the background music becomes tense. This technology makes the gaming experience more realistic and fun!
Glossary
Hierarchical Visual-Grounded Captioning (HVGC)
A framework for generating disentangled captions for video and audio, eliminating modal interference.
Used to address modal interference issues.
BridgeDiT
A dual-tower diffusion transformer employing a Dual Cross-Attention mechanism to achieve synchronization.
Used to achieve semantic and temporal synchronization.
Dual Cross-Attention (DCA)
An interaction mechanism for dual-tower models allowing bidirectional information exchange.
Used to achieve video and audio synchronization.
Fréchet Video Distance (FVD)
A metric for evaluating the quality of generated videos; lower values indicate better quality.
Used to assess video generation quality.
Fréchet Audio Distance (FAD)
A metric for evaluating the quality of generated audio; lower values indicate better quality.
Used to assess audio generation quality.
Open Questions Unanswered questions from this research
- 1 How to maintain high-quality audio generation in complex background sounds? Current methods perform poorly in this aspect, requiring more research.
- 2 How to reduce the computational cost of the model? Current methods have high hardware resource requirements.
Applications
Immediate Applications
Film Production
This technology can be used in film production to enhance the quality and synchronization of audio-visual content.
Long-term Vision
Virtual Reality
Applying this technology in virtual reality can enhance the immersive experience for users.
Abstract
This study focuses on a challenging yet promising task, Text-to-Sounding-Video (T2SV) generation, which aims to generate a video with synchronized audio from text conditions, meanwhile ensuring both modalities are aligned with text. Despite progress in joint audio-video training, two critical challenges still remain unaddressed: (1) a single, shared text caption where the text for video is equal to the text for audio often creates modal interference, confusing the pretrained backbones, and (2) the optimal mechanism for cross-modal feature interaction remains unclear. To address these challenges, we first propose the Hierarchical Visual-Grounded Captioning (HVGC) framework that generates pairs of disentangled captions, a video caption, and an audio caption, eliminating interference at the conditioning stage. Based on HVGC, we further introduce BridgeDiT, a novel dual-tower diffusion transformer, which employs a Dual CrossAttention (DCA) mechanism that acts as a robust ``bridge" to enable a symmetric, bidirectional exchange of information, achieving both semantic and temporal synchronization. Extensive experiments on three benchmark datasets, supported by human evaluations, demonstrate that our method achieves state-of-the-art results on most metrics. Comprehensive ablation studies further validate the effectiveness of our contributions, offering key insights for the future T2SV task. All the codes and checkpoints will be publicly released.