![]() ![]() The most challenging part of this type of video editing is maintaining temporal consistency between frames. Quality stands for the definition of the image, such as the presence of fine-grained details. Fidelity accounts for the degree of preservation of the original input content (or at least of that portion not referred to in the text prompt). Alignment refers to the degree of consistency between the input text prompt and the outcome video. The goodness of a technique can be described by alignment, fidelity, and quality. To date, only a few models have been employed for this task, and with scarce results. Some recent approaches have been developed to fill this gap, focusing on preserving particular image characteristics, such as facial features, background, or foreground, while editing others.įor video editing, the situation changes. ![]() ![]() □ JOIN the fastest ML Subreddit CommunityĪlthough these models offer new creative processes, they are mostly constrained to creating novel images rather than editing existing ones. These models have shown impressive results in recent years, achieving state-of-the-art text-to-image and text-to-video synthesis performance. Diffusion models have emerged as a powerful technique for addressing this problem, leveraging the power of deep neural networks to generate photo-realistic images that align with a given textual description or video frames with temporal consistency.ĭiffusion models work by iteratively refining the generated content through a sequence of diffusion steps, where the model learns to capture the complex dependencies between the textual and visual domains. If text-to-image is already challenging, text-to-video synthesis extends the complexity of 2D content generation to 3D, given the temporal dependencies between video frames.Ī classic approach when dealing with such complex content is exploiting diffusion models. Generating high-quality visual content from textual descriptions requires capturing the intricate relationship between language and visual information. Text-to-image is a challenging task in computer vision and natural language processing. ![]()
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