VIDU Launches AI Video Feature for Subject Consistency

September 11, 2024 — VIDU, a text-to-image video multimodal model developed by ShengShu Technology, has introduced a new feature called Subject Consistency. This feature is designed to address a common issue in AI-generated video content: maintaining consistent visual elements throughout the production process.

The Subject Consistency feature helps ensure that characters, settings, and objects generated by AI remain consistent from scene to scene. In previous versions of AI video tools, there were instances where visual elements would change unpredictably, resulting in an inconsistent final product. With this update, VIDU aims to minimize such issues, providing users with more reliable results.

The feature works by using machine learning algorithms that analyze the input provided by users and ensure continuity across different stages of video creation. This improvement is expected to benefit various sectors, including education, marketing, entertainment, and social media, where video consistency is important for storytelling and branding.

“This achievement is a testament to the incredible dedication and innovation of our team,” said Jiayu Tang, Cofounder and CEO of Shengshu Technology. “We’re proud to bring this feature to market and believe it will significantly enhance how our users interact with and utilize AI in their creative processes.”

The update is intended to offer creators greater control over their projects and reduce the need for manual adjustments during the editing process. By automating the task of maintaining visual consistency, VIDU hopes to improve the overall efficiency of AI-powered video production.

VIDU is known for its AI-driven solutions that allow users to create professional-quality videos with minimal technical expertise. The platform serves a wide range of users, from social media content creators to businesses looking to streamline their video production efforts.

SEE ALSO: Vidu Launches Globally, with Baidu’s AIHC Support for Large-Scale Video Model Training