KLING AI Introduction

KLING AI utilizes a sophisticated deep learning model, specifically Deep Convolutional Neural Networks (DCNNs), which are adept at processing and generating visual data. A key innovation is its 3D spatiotemporal joint attention mechanism, allowing the AI to better understand and model motion in three-dimensional space over time. The system employs a unique 3D Variational Autoencoder (VAE) for face and body reconstruction, enabling detailed expression and limb movement from a single full-body image. KLING AI also adopts a variable resolution training strategy, allowing for output in various video aspect ratios to meet diverse usage scenarios. Based on a deep understanding of text-video semantics and the Diffusion Transformer architecture, it can transform users' imagination into concrete visuals, even creating fictional scenes not found in the real world.