Shape Disentanglement

Shape disentanglement aims to decompose complex data, such as images or 3D models, into independent factors representing underlying attributes like shape, pose, color, and texture. Current research focuses on developing generative models, including variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models, often incorporating neural radiance fields (NeRFs) for 3D data, to achieve this separation. This disentanglement improves controllability in generative tasks, enhances the interpretability of learned representations, and facilitates downstream applications like robust image synthesis, domain generalization, and interpretable 3D shape analysis. The ability to isolate and manipulate these factors promises significant advancements in computer vision, graphics, and AI.