Latent Direction

Latent direction research focuses on identifying and manipulating meaningful directions within the latent spaces of generative models, primarily GANs and diffusion models, to achieve fine-grained control over image generation and editing. Current research emphasizes unsupervised and supervised methods for discovering these directions, often leveraging techniques like Jacobian analysis, principal component analysis, and disentanglement losses to isolate specific attributes or concepts. This work is significant for improving the controllability and interpretability of generative models, leading to advancements in applications such as image editing, bias mitigation, and human motion prediction.