Shape Latent

Shape latent research focuses on representing the complex geometry of 3D shapes using compact, low-dimensional codes, enabling efficient storage, manipulation, and generation. Current efforts concentrate on developing novel neural network architectures, including variational autoencoders (VAEs), diffusion models, and implicit neural representations, to learn these shape latents, often incorporating techniques like contrastive learning and normalizing flows to improve representation quality and diversity. This work is significant for advancing 3D modeling, computer graphics, and related fields by enabling more efficient and effective processing of large-scale 3D data, facilitating applications such as shape compression, generation, and manipulation.