arXiv:2403.07938v1 Announce Type: cross
Abstract: In recent times, the focus on text-to-audio (TTA) generation has intensified, as researchers strive to synthesize audio from textual descriptions. However, most existing methods, though leveraging latent diffusion models to learn the correlation between audio and text embeddings, fall short when it comes to maintaining a seamless synchronization between the produced audio and its video. This often results in discernible audio-visual mismatches. To bridge this gap, we introduce a groundbreaking benchmark for Text-to-Audio generation that aligns with Videos, named T2AV-Bench. This benchmark distinguishes itself with three novel metrics dedicated to evaluating visual alignment and temporal consistency. To complement this, we also present a simple yet effective video-aligned TTA generation model, namely T2AV. Moving beyond traditional methods, T2AV refines the latent diffusion approach by integrating visual-aligned text embeddings as its conditional foundation. It employs a temporal multi-head attention transformer to extract and understand temporal nuances from video data, a feat amplified by our Audio-Visual ControlNet that adeptly merges temporal visual representations with text embeddings. Further enhancing this integration, we weave in a contrastive learning objective, designed to ensure that the visual-aligned text embeddings resonate closely with the audio features. Extensive evaluations on the AudioCaps and T2AV-Bench demonstrate that our T2AV sets a new standard for video-aligned TTA generation in ensuring visual alignment and temporal consistency.
Bridging the Gap between Text-to-Audio Generation and Video Alignment
In the field of multimedia information systems, text-to-audio (TTA) generation has gained increasing attention. Researchers are continuously striving to synthesize high-quality audio content from textual descriptions. However, one major challenge faced by existing methods is the lack of seamless synchronization between the generated audio and its corresponding video, resulting in noticeable audio-visual mismatches. To address this issue, a groundbreaking benchmark called T2AV-Bench has been introduced to evaluate the visual alignment and temporal consistency of TTA generation models aligned with videos.
The T2AV-Bench benchmark is designed to bridge the gap by offering three novel metrics dedicated to assessing visual alignment and temporal consistency. These metrics serve as a robust evaluation framework for TTA generation models. By leveraging these metrics, researchers can better understand and improve the performance of their models in terms of audio-visual synchronization.
In addition to the benchmark, a new TTA generation model called T2AV has been presented. T2AV goes beyond traditional methods by incorporating visual-aligned text embeddings into its latent diffusion approach. This integration allows T2AV to effectively capture temporal nuances from video data, ensuring a more accurate and natural alignment between the generated audio and the video content. This is achieved through the utilization of a temporal multi-head attention transformer, which extracts and understands temporal information from the video data.
T2AV also introduces an innovative component called the Audio-Visual ControlNet, which merges temporal visual representations with text embeddings. This integration enhances the overall alignment and coherence between the audio and video components. To further improve the synchronization, a contrastive learning objective is employed to ensure that the visual-aligned text embeddings closely resonate with the audio features.
The evaluations conducted on the AudioCaps and T2AV-Bench datasets demonstrate the effectiveness of the T2AV model. It sets a new standard for video-aligned TTA generation by significantly improving visual alignment and temporal consistency. These advancements have direct implications for various applications in the field of multimedia systems, such as animations, artificial reality (AR), augmented reality (AR), and virtual reality (VR).
The multi-disciplinary nature of the concepts presented in this content showcases the intersection between natural language processing, computer vision, and audio processing. The integration of these disciplines is crucial for developing more advanced and realistic TTA generation models that can seamlessly align audio and video content. By addressing the shortcomings of existing methods and introducing innovative techniques, this research paves the way for future advancements in multimedia information systems.