ModelScope Text-to-Video Technical Report (2308.06571v1)

Abstract: This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i.e., Stable Diffusion). ModelScopeT2V incorporates spatio-temporal blocks to ensure consistent frame generation and smooth movement transitions. The model could adapt to varying frame numbers during training and inference, rendering it suitable for both image-text and video-text datasets. ModelScopeT2V brings together three components (i.e., VQGAN, a text encoder, and a denoising UNet), totally comprising 1.7 billion parameters, in which 0.5 billion parameters are dedicated to temporal capabilities. The model demonstrates superior performance over state-of-the-art methods across three evaluation metrics. The code and an online demo are available at \url{https://modelscope.cn/models/damo/text-to-video-synthesis/summary}.

• The paper introduces ModelScopeT2V, a diffusion-based text-to-video synthesis model that uses spatio-temporal blocks to ensure smooth frame transitions and spatial coherence.
• The paper details a scalable architecture integrating VQGAN, a text encoder, and a denoising UNet, pre-trained on diverse image-text and video-text datasets for enhanced semantic richness.
• The paper demonstrates robust performance with superior FID-vid, FVD, and CLIPSIM scores, setting a strong baseline for future research in automated video generation.

An Academic Overview of ModelScope Text-to-Video Technical Report

The paper entitled "ModelScope Text-to-Video Technical Report," authored by Jiuniu Wang et al., introduces a state-of-the-art text-to-video synthesis model named ModelScopeT2V. This model is an evolution from existing text-to-image synthesis models, leveraging the architecture of stable diffusion processes. The development of ModelScopeT2V emphasizes the automated generation of video content in response to textual input, embedding both spatial and temporal coherence through the innovative design of spatio-temporal blocks.

Core Contributions

The main contributions of the paper revolve around the advancement of text-to-video generation methodologies through the development of ModelScopeT2V. Central to this model are three interconnected components: VQGAN, a text encoder, and a denoising UNet. The architectural innovation lies in the incorporation of spatio-temporal blocks to ensure frame consistency and the smooth transition of motion across frames, distinguishing the model from traditional text-to-image models.

1. Spatio-Temporal Considerations: The integration of spatio-temporal blocks enables the model to handle both spatial dependencies and temporal dynamics effectively. This allows for the generation of videos that are coherent not only in terms of spatial consistency within frames but also in terms of smooth motion across successive frames.
2. Scalable Model Structure: ModelScopeT2V is designed with scalability in mind, being adaptable to datasets of varying frame numbers. This makes it well-suited to utilize both image-text and video-text paired datasets, thereby improving semantic richness and adaptability.
3. Pre-training on Diverse Datasets: The model is pre-trained using a novel multi-frame training strategy, enhancing its robustness by drawing semantic insights from extensive image-text datasets like LAION and video-text paired datasets such as WebVid.

Quantitative and Qualitative Evaluation

The paper details rigorous evaluations of ModelScopeT2V in comparison to state-of-the-art text-to-video synthesis models based on metrics such as FID-vid, FVD, and CLIPSIM, illustrating its superior performance. ModelScopeT2V demonstrates competitive results, attaining notable marks on these benchmarks, indicating its capacity to produce videos that are not only visually coherent and semantically aligned but also comparable to existing leading methods.

Qualitative evaluations further showcase the model's capabilities, comparing the generated videos against those produced by prominent models such as Make-A-Video and Imagen Video. The results highlight ModelScopeT2V’s ability to capture a wider range of motion and maintain a high degree of realism in generated videos.

Implications and Future Directions

The research underscores the potential application of diffusion models for text-driven video generation. By making the source code publicly accessible and offering online demos, the authors contribute to the open-source community, facilitating further research and development in this domain.

Future research could explore the integration of additional conditions to refine video quality. Techniques such as multi-condition methodologies or the LoRA technique could be employed to further harness the model's potential in producing nuanced and high-fidelity video content. Additionally, extending the model’s capabilities towards generating longer, semantically richer videos could significantly broaden its applicability.

The ModelScopeT2V marks a significant advancement in the field of video synthesis, serving as a robust baseline for both academic and practical explorations into automated video generation systems driven by textual descriptions. This work paves the way for future innovations and comprehensive research in merging linguistic and visual modalities.