Goku is a multimodal generation model that integrates advanced technologies, and its core technical architecture is based on Flow Transformation. The model realizes dynamic interactions between images and video markers through an innovative Flow Transformation formulation, which significantly improves the coherence and detail of the generated content. The Flow Transformation technique allows the model to establish smooth transitions between video frames in latent space, which solves the problem of frame jumping that is common in traditional methods.
As a co-generative model, Goku has the ability to process both still images and dynamic videos. This design breaks through the limitations of traditional unimodal generators and allows the model to share the underlying feature representations of both images and videos, thus improving the efficiency of data utilization. Experimental data shows that in standard benchmark tests, Goku's video generation quality outperforms the baseline model by 231 TP3T, especially in fine-grained features such as character expressions and object textures.
Industry applications demonstrate that the technology's federated architecture is particularly suitable for scenarios that require cross-modal transformations, such as transforming merchandise posters (images) into dynamic advertisements (videos). The parameter sharing mechanism within the model ensures the effectiveness of knowledge migration between different generation tasks.
This answer comes from the articleGoku: Generates detailed and consistent videos, ideal for creating commercials with detailed characters and objects.The
