Vidu: Vidu 2.0 Start-End to Video API

Vidu 2.0 Start-End to Video is the start-end frame-driven video generation model of the Vidu 2.0 generation developed by Shengshu Technology. As the flagship start-end frame video generation version under the Vidu digital version naming system (distinct from the subsequent Q series quarterly naming system), Vidu 2.0 is a landmark version launched after Shengshu Technology completed an important generational leap in core video generation technical capabilities, achieving comprehensive and systematic improvements over the Vidu 1.x series in motion quality, visual consistency, semantic understanding capability, and overall video generation quality. The model accepts simultaneously provided starting frame and ending frame images, combined with optional text descriptions, automatically inferring and generating high-quality transition video content between the two frames, establishing the comprehensive technical baseline for Vidu's start-end frame video generation capability in the 2.0 generation. Key capabilities include: 2.0 Generation Start-End Frame Transition Generation Quality: Using the starting and ending frames as dual visual anchor points, fully leveraging the technical advantages of the Vidu 2.0 core generation architecture to generate transition video content reaching the 2.0 generation's comprehensive standards in motion naturalness, picture quality, visual coherence, and transition rationality. Compared to the 1.x series, the 2.0 version achieves significant generational improvements in start-end frame fidelity, subject stability, and picture detail precision. Enhanced Visual Anchor Point Constraint and Feature Fidelity: Based on Vidu 2.0's enhanced visual understanding and generation alignment capabilities, achieves more precise and comprehensive constraint processing of the visual features of starting and ending frames, effectively reducing the probability of subject feature drift, appearance inconsistency, and picture jumping in generated transition videos, maintaining a higher level of visual consistency with input dual frames throughout the entire transition video. 2.0 Generation Motion Inference and Dynamic Generation: Based on Vidu 2.0's generational progress in motion modeling, performs more rational and fluid inference and generation of motion trajectories, object dynamics, and scene evolution between starting and ending frames, producing transition video content superior to the 1.x series in physical rationality, motion fluidity, and dynamic visual expressiveness. Enhanced Semantic Understanding and Text Guidance: Possesses significantly enhanced Chinese and English prompt semantic understanding and instruction execution capabilities of the Vidu 2.0 generation, more accurately converting text descriptions into effective control over start-end frame transition content, achieving deeper integration of text semantics with dual-frame visual content, providing more precise and reliable generation content controllability compared to the 1.x series. Comprehensively Improved Video Output Quality: Achieves comprehensive improvements over the 1.x series in resolution performance, picture clarity, color reproduction, light and shadow texture, and overall visual quality, with the overall visual quality of generated videos reaching the 2.0 generation's comprehensive standards, capable of meeting the needs of a broader range of professional content creation and commercial applications. Typical use cases include high-quality transition generation in professional video content creation, brand advertising and marketing video transition production, film and television concept pre-visualization and animatic production, character and product state change animation display, artistic creative video content production, and commercial content creation workflows with certain professional requirements for video generation quality.