Vidu: Vidu 2.0 Image to Video API

Vidu 2.0 Image-to-Video is the second-generation flagship image-driven video generation model developed by Shengshu Technology, representing an important milestone version in the development history of the Vidu series. Compared to the Vidu 1.x series, Vidu 2.0 achieves comprehensive upgrades across core dimensions including model architecture, training data scale, motion generation quality, and semantic understanding capabilities, marking the entry of Shengshu Technology's image-to-video technology into a new development stage. The model uses static images as visual anchors, combined with optional text descriptions, to generate video content with high-quality dynamic effects, smooth motion performance, and strong visual consistency. Key capabilities include: Comprehensively Upgraded Image Feature Fidelity: Vidu 2.0 shows significant improvement in image visual feature preservation compared to its predecessor, more accurately maintaining the consistency of facial features, clothing details, scene elements, and overall color style in input images, effectively reducing character drift and visual distortion in generated videos, ensuring high visual coherence between source images and generated videos. Significantly Improved Motion Generation Quality: Compared to the Vidu 1.x series, version 2.0 shows notable progress in the naturalness, fluidity, and rationality of motion generation, with more naturally coordinated character actions, ergonomically compliant limb movements, physically law-abiding object motion, and camera movements with stronger cinematic feel and professional texture. Enhanced Semantic Understanding and Image-Text Fusion: Possesses deep semantic understanding capability for complex Chinese and English prompts, more accurately parsing and executing refined motion instructions, scene descriptions, and stylistic guidance, achieving closer integration of text semantics with image visual content, improving the controllability and accuracy of generated videos. Richer Motion Type Coverage: Supports generation of a wider range of motion types, including complex character actions, group movements, camera pan/tilt/zoom/dolly, depth-of-field changes, dynamic light and shadow evolution, and other high-difficulty dynamic effects, greatly expanding the application boundaries of image-to-video generation. Overall Image Quality Improvement: Shows obvious improvement over its predecessor in image resolution, picture clarity, color reproduction, light and shadow texture, and detail precision, with the overall visual quality of generated videos closer to professional film and television production standards. Typical use cases include professional AI video content creation, brand advertising and marketing video production, portrait and product animation display, film concept pre-visualization, artistic creation, and digital media content production.