The exponential rise in the popularity of Artificial Intelligence (AI) in recent times has led to some great advancements in deep generative models. These models have been applied to the field of video generation to create images and synthesize pictures. The well-known examples are the autoregressive models, such as GANs and VAEs, which sparked a wave of interest among the AI community in using comparable techniques to create videos.
Using deep generative models for video generation comes with challenges as due to their small scale; their application is restricted to particular areas, including face or body generation. However, new advances in large-scale diffusion models and processing capacity have opened up more options for producing videos in broader contexts. Even with the advancements, problems remain to be solved, like producing movies with a cinematic visual quality and dealing with problems like temporal coherence and subject continuity, particularly in lengthy videos.
To overcome the challenges, a team of researchers has introduced Vchitect, a large-scale generalist video creation system intended for Text-to-Video (T2V) and Image-to-Video (I2V) applications. This system has been designed with the aim of synthesizing movies with varying lengths and a cinematic visual aesthetic in order to facilitate smooth camera movements and narrative coherence.
Vchitect can create high-definition videos of any duration, from a few seconds to several minutes. It ensures smooth transitions between scenes and supports consistent storytelling. The system integrates multiple models to cater to distinct facets of video production, which are as follows.
- LaVie, Text-to-Video Model (T2V): This serves as the foundational paradigm for Vchitect, which transforms written descriptions into brief, excellent movies.
- SEINE, Image-to-Video (I2V) Generation Model: The system’s adaptability is increased by this feature, which enables it to produce dynamic content from static photos.
- The Short-to-Long (S2L) Model: It creates seamless connections and transitions between short movies. It enhances the overall coherence and flow of longer videos for a more engaging watch.
- Subject-Consistent Model: This model can produce videos with the same subject. Maintaining coherence between separate footage is crucial, particularly when the same person or object appears in multiple movie segments.
- Temporal Interpolation Model: It improves the smoothness of motion in the produced videos and enhances the video content’s overall flow by enhancing the temporal characteristics.
- Video Super-Resolution Model: This model improves the resolution of the produced videos while also addressing spatial visual quality. This is crucial to guaranteeing the clarity and excellent quality of the visual elements.
The team has also curated a comprehensive and diverse video dataset called Vimeo25M. With 25 million text-video pairings, this collection prioritizes visual appeal, diversity, and quality. The team has shared that in order to ensure that the models are adequately trained and capable of handling a wide range of events and content types, a broad and diverse dataset must be included.
A comprehensive analysis has also been conducted which shows how the base T2V model in the Vchitect system is preferable. Aspects like visual quality, coherence, and the capacity to produce movies that correspond with the given verbal descriptions have been included in this evaluation.
Check out the LaVie (Text2Video Model) Project, Paper, SEINE (Image2Video Model) Project, and Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to join our 33k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.
Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.