Enhancing Text-to-Video Generation with Swapped Spatiotemporal Attention

Concepts de base

The core message of this paper is that strengthening the interaction between spatial and temporal features is crucial for achieving high-quality text-to-video generation. The authors propose a novel Swapped spatiotemporal Cross-Attention (Swap-CA) mechanism that alternates the "query" role between spatial and temporal blocks, enabling mutual reinforcement for each other.

Résumé

The paper addresses the challenges in open-domain text-to-video generation, such as modeling the complex relationship between space and time, and the lack of large-scale text-video paired data.

Key highlights:

• The authors reveal the significance of jointly modeling space and time for video generation, and introduce a novel Swap-CA mechanism to reinforce both spatial and temporal interactions.
• They curate the first open-source dataset comprising 130 million text-video pairs (HD-VG-130M), supporting high-quality video generation with high-definition, widescreen, and watermark-free characters. They further create a higher-quality 40M subset (HD-VG-40M) by considering text, motion, and aesthetic factors.
• Experimental results demonstrate the superiority of their approach in terms of per-frame quality, temporal correlation, and text-video alignment, outperforming existing methods.

Stats

"Generating videos guided by text instructions poses significant challenges, such as modeling the complex relationship between space and time, and the lack of large-scale text-video paired data."
"Videos shorter than 2 seconds lack sufficient frames for extraction at 2 FPS, so we exclude them when constructing the higher-quality subset."
"To eliminate these instances, we apply a filtering rule of Oavg > 0.2, resulting in the removal of 3.71% of videos."
"We finally employ a filtering strategy in which we keep videos satisfying either Oavg/Omd < 2 or Omd > 6, which is able to remove image transformation animations while retaining real-world camera transformations. It removes 9.58% of videos from the dataset."

Citations

"To fully unlock model capabilities for high-quality video generation and promote the development of the field, we curate a large-scale and open-source video dataset called HD-VG-130M."
"By deeply interplaying spatial and temporal features through the proposed swap attention, we present a holistic VideoFactory framework for text-to-video generation."
"Experimental quantitative and qualitative results demonstrate the superiority of our approach in terms of per-frame quality, temporal correlation, and text-video alignment, with clear margins."

Idées clés tirées de

Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation

by Wenjing Wang... à arxiv.org 04-09-2024

https://arxiv.org/pdf/2305.10874.pdf

Questions plus approfondies

How can the proposed Swap-CA mechanism be further extended to other video-related tasks beyond text-to-video generation, such as video understanding or video editing?

The Swap-CA mechanism, which enhances the interaction between spatial and temporal features in text-to-video generation, can be extended to other video-related tasks to improve performance and efficiency. For video understanding tasks, the Swap-CA mechanism can be utilized to strengthen the connection between spatial and temporal features in video analysis. By incorporating Swap-CA into video understanding models, the system can better capture the relationships between objects, actions, and contexts in videos, leading to more accurate and comprehensive video understanding.
In the context of video editing, the Swap-CA mechanism can be applied to facilitate the alignment of spatial and temporal elements in the editing process. By incorporating Swap-CA into video editing software, editors can have a more intuitive and efficient way to manipulate both spatial and temporal aspects of videos. This can lead to smoother transitions, better synchronization of audio and visual elements, and overall improved editing workflow.
The key to extending the Swap-CA mechanism to these tasks lies in adapting the mechanism to the specific requirements and objectives of each task. By customizing the implementation of Swap-CA for video understanding and video editing applications, it can significantly enhance the performance and capabilities of these systems.

How can the proposed Swap-CA mechanism be further extended to other video-related tasks beyond text-to-video generation, such as video understanding or video editing?

The current dataset construction approach has several potential limitations that could be addressed to better capture the diversity and complexity of real-world videos. One limitation is the reliance on YouTube videos, which may not always represent a wide range of video styles, genres, and qualities. To improve dataset diversity, additional sources of videos from different platforms and genres could be included in the dataset construction process.
Another limitation is the focus on high-definition, widescreen, and watermark-free videos, which may not fully represent the variety of video qualities found in real-world scenarios. To address this, a more comprehensive dataset curation process could involve including videos with varying resolutions, aspect ratios, and visual qualities to better reflect the real-world video landscape.
Furthermore, the dataset construction approach may not adequately account for cultural or regional differences in video content. To enhance dataset diversity and inclusivity, efforts could be made to incorporate videos from a more diverse range of cultural backgrounds and languages, ensuring a more representative dataset for training video generation models.
Overall, improving the dataset construction approach to address these limitations would result in a more robust and comprehensive dataset that better captures the diversity and complexity of real-world videos, leading to more effective training and evaluation of video generation models.

How can the aesthetic evaluation and filtering be further improved to better align with human perception and preferences?

Aesthetic evaluation and filtering play a crucial role in video generation, as they directly impact the visual quality and appeal of the generated videos. To better align with human perception and preferences, the aesthetic evaluation process can be further improved in the following ways:

Human-in-the-loop Evaluation: Incorporating human evaluators in the aesthetic assessment process can provide valuable insights into subjective aesthetic preferences. By collecting feedback from human raters on the visual quality and appeal of generated videos, the evaluation process can better reflect human perception.

Fine-tuning Aesthetic Models: Continuously training and fine-tuning aesthetic evaluation models on a diverse range of high-quality videos can help improve their accuracy in assessing visual aesthetics. By incorporating feedback from human evaluators, the models can learn to better align with human preferences.

Incorporating Psychological Principles: Drawing from principles of visual psychology and design theory, the aesthetic evaluation process can be enhanced to consider factors such as color harmony, composition, balance, and visual hierarchy. By incorporating these principles, the evaluation criteria can better align with human perception and preferences.

Cultural Sensitivity: Considering cultural differences in aesthetic preferences is essential for aligning with diverse human perceptions. By incorporating cultural sensitivity into the aesthetic evaluation process, the criteria can be adjusted to reflect a broader range of aesthetic preferences across different cultures and regions.

By implementing these strategies, the aesthetic evaluation and filtering process can be further improved to better align with human perception and preferences, ultimately leading to the generation of visually appealing and engaging videos.