Visual Consistency
Visual consistency in image and video generation aims to maintain coherent visual attributes—like appearance, style, and spatial relationships—across generated content, addressing a major challenge in computer vision. Current research focuses on developing novel model architectures and algorithms, such as diffusion models and contrastive learning methods, often incorporating attention mechanisms and dynamic feature transformations to improve consistency. These advancements are crucial for enhancing the realism and quality of generated images and videos, with applications ranging from image editing and animation to robotics and virtual reality. The ultimate goal is to create more natural and believable synthetic media.
Papers
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