Intelligent conversion system from static to dynamic
The Sketch-to-Film technology adopted by the platform is constructed based on a deep learning framework and contains three core technology layers: firstly, it recognizes the spatial topology of sketches through convolutional neural network (CNN), then it uses spatio-temporal generative adversarial network (ST-GAN) to predict reasonable motion trajectories, and finally it uses neural renderer (NR) to synthesize light and shadow effects in accordance with physical laws. The system supports various input forms such as pencil sketches and digital line drawings, and automatically recognizes key elements such as character joints and architectural perspective.
Applications in the field of education have proved that teachers drawing schematic diagrams of biological cell division can generate dynamic teaching animation after processing by the system, with a correct rate of 92%. Adobe's test report points out that compared with the traditional frame-by-frame production method, the technology compresses the animation production time from 40 hours/minute to 15 minutes/minute. The platform also provides a library of motion templates so that users can directly apply preset motion patterns such as 'walking cycle' or 'fluid simulation'.
This answer comes from the articleOpenCreator: integrating multiple AI models to generate creative videosThe
