Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising

Abstract: Leveraging large-scale image-text datasets and advancements in diffusion models, text-driven generative models have made remarkable strides in the field of image generation and editing. This study explores the potential of extending the text-driven ability to the generation and editing of multi-text conditioned long videos. Current methodologies for video generation and editing, while innovative, are often confined to extremely short videos (typically less than 24 frames) and are limited to a single text condition. These constraints significantly limit their applications given that real-world videos usually consist of multiple segments, each bearing different semantic information. To address this challenge, we introduce a novel paradigm dubbed as Gen-L-Video, capable of extending off-the-shelf short video diffusion models for generating and editing videos comprising hundreds of frames with diverse semantic segments without introducing additional training, all while preserving content consistency. We have implemented three mainstream text-driven video generation and editing methodologies and extended them to accommodate longer videos imbued with a variety of semantic segments with our proposed paradigm. Our experimental outcomes reveal that our approach significantly broadens the generative and editing capabilities of video diffusion models, offering new possibilities for future research and applications. The code is available at https://github.com/G-U-N/Gen-L-Video.

• The paper introduces Temporal Co-Denoising to extend short video diffusion models without additional training.
• It decomposes long videos into overlapping short segments to maintain frame consistency and embed multiple semantic cues.
• The approach enhances video generation and editing flexibility, benefiting industries like entertainment, VR, and digital media.

Overview of Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising

The paper lays the groundwork for a novel approach to text-driven video generation and editing, specifically targeting the challenges associated with creating long videos from multiple text conditions. Traditional video generation approaches face significant limitations, primarily in their capacity to handle short video lengths and single-text conditions. These constraints are at odds with real-world scenarios where videos often consist of hundreds of frames with varied semantic information.

The authors introduce a paradigm named Gen-L-Video, which extends the capabilities of short video diffusion models without necessitating additional training. The primary technical innovation is termed "Temporal Co-Denoising." This mechanism permits the integration of short video models for generating long videos, maintaining content consistency, and supporting multiple semantic segments. The key contribution here is the abstraction of long video generation as a collection of overlapping short video segments, allowing for parallel denoising and temporal synchronization.

Methodological Advances

Three existing methodologies for text-driven video generation are leveraged and enhanced in this work:

1. Pretrained Text-to-Video (t2v): Incorporates models trained on extensive text-video datasets, ensuring inter-frame consistency through temporal interaction modules. The paper illustrates how these can be adapted to support longer videos with varied semantic content.
2. Tuning-free t2v: Engages pre-trained Text-to-Image models for frame-by-frame generation, adding controls for cross-frame consistency. The innovation here lies in efficiently extending these pre-trained models to longer sequences.
3. One-shot tuning t2v: This method fine-tunes pre-trained Text-to-Image models on a specific video, learning motions or contents specific to that example. The paper shows how these models can be used to render long videos without losing flexibility.

Implications and Future Directions

The successful application of Gen-L-Video broadens the scope of existing video diffusion models, offering solutions to traditional limitations such as frame length and singular text conditioning. The results presented show consistent improvements in generative capabilities and editing flexibility, particularly when integrating advanced object detection and segmentation technologies like Grounding DINO and SAM.

One significant implication is the potential to transform various industries that rely on precise video content generation. This includes entertainment, virtual reality, and corporate media, where long, thematically diverse video content is often required. Additionally, the framework could foster more interactive and visually dynamic content creation tools, empowering creators with more refined control over the generated content.

Future developments could explore the co-dissemination of diverse diffusion models, allowing a mélange of video generation styles and methodologies to coexist effectively. Furthermore, the integration of real-time processing and personalization could refine the Gen-L-Video framework, making it adaptable to user-specific needs and platforms.

In summary, the Gen-L-Video paradigm addresses key limitations in video generation technologies, introducing an efficient and versatile framework capable of handling longer and more semantically rich video content. Its contribution to the field lies in improving the methodological approach to video generation, setting the stage for future innovations across a breadth of applications.