Videos are a commonly used digital medium prized for its ability to present vivid and engaging visual experiences. With the ubiquitous use of smartphones and digital cameras, recording live events on camera has become simple. However, the process becomes significantly more difficult and expensive when a video is produced to represent the idea visually. This often requires professional experience in computer graphics, modeling, and creating animations. Fortunately, new developments in text to video conversion have made it possible to streamline this procedure using only text prompts.
Figure 1 shows how the model can produce temporally coherent movies that adhere to guiding intentions when given text descriptions and motion structure as inputs. They demonstrate the results of video production in various applications, including (top) setting up real-world scenes in video, (middle) dynamic modeling of 3D scenes in video, and (bottom) playing video, using guide construction. structure from various sources.
They argue that while language is a well-known and flexible description tool, it may need to be more successful in providing precise control. Instead, it excels at communicating an abstract global context. This encourages us to investigate creating personalized videos using text to describe the setting and movement in a specific direction. Because frame depth maps are 3D-compatible 2D data well-suited to the task of video creation, they are specifically chosen to describe the structure of motion. The direction of the structure in your method can be relatively basic so that non-experts can easily prepare it.
This architecture gives the generative model the freedom to generate realistic content without relying on meticulously produced input. For example, the creation of a photorealistic outdoor environment can be guided by a stage setup using products found in an office (Figure 1 (above)). Physical objects can be replaced with specific geometric parts or any readily available 3D assets using 3D modeling software (Figure 1 (middle)). Using the depth calculated from already existing recordings is another option (Figure 1 (below)). To customize their movies as intended, users have flexibility and control thanks to the combination of textual and structural instructions.
To do this, researchers from CUHK, Tencent AI Lab, and HKUST use a latent diffusion model (LDM), which adopts a diffusion model in a lower-dimensional reduced latent space to reduce processing costs. They suggest separating the training of spatial modules (for image synthesis) and temporal modules (for temporal coherence) for an open world video production model. This design is based on two main factors: (i) training the model components separately reduces computational resource requirements, which is especially important for resource-intensive tasks; and (ii) since image datasets encompass a much wider variety of concepts than existing video datasets, pretraining the model for image synthesis helps to inherit the various visual concepts and transfer them to the image. video generation.
Achieving temporal consistency is an important task. They keep these as frozen spatial blocks and introduce temporal blocks designed to learn inter-frame coherence across the entire video dataset using a pre-trained image LDM. In particular, they incorporate spatial and temporal convolutions, which increases the flexibility of the pretrained modules and improves temporal stability. In addition, they use a simple but powerful causal attention mask method to allow for longer video synthesis (ie, four times the training period), which greatly reduces the risk of quality deterioration.
Qualitative and quantitative evaluations show that the suggested technique outperforms the baselines, especially in terms of temporal consistency and fidelity to user instructions. The efficiency of the proposed designs, which are essential for the working of the approach, is supported by ablation experiments. In addition, they demonstrated several fascinating applications that their methodology made possible, and the results illustrate the potential for real-world applications.
The following is a summary of their contributions: • They offer textual and structural assistance to present an effective method for producing personalized videos. His approach produces the best results in both quantitative and qualitative terms for regulated text-to-video production. • Provide a method for using pretrained image BOMs to generate videos that inherit rich visuals and have good temporal consistency. • Include a temporal masking approach to extend the duration of video synthesis and minimize quality loss.
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Aneesh Tickoo is a consulting intern at MarktechPost. She is currently pursuing her bachelor’s degree in Information Science and Artificial Intelligence at the Indian Institute of Technology (IIT), Bhilai. She spends most of her time working on projects aimed at harnessing the power of machine learning. Her research interest is image processing and she is passionate about creating solutions around her. She loves connecting with people and collaborating on interesting projects.
