The recent advancements in 3D modeling and compression techniques have significantly pushed the boundaries of what is possible in various applications. Researchers are increasingly turning to implicit neural representations to handle complex geometries and attributes, offering more efficient and accurate solutions. This approach is particularly evident in the compression of point clouds, where novel frameworks are outperforming traditional methods by leveraging neural networks to represent voxelized data. Additionally, there is a notable focus on enhancing the quality of dynamic point cloud data, with innovations in color enhancement and temporal redundancy reduction. In the realm of medical visualization, automated pipelines are being developed to provide accurate previews of facial surgeries, improving patient consultations and outcomes. These developments not only advance the technical capabilities but also have practical implications in fields such as medicine and entertainment. Notably, the introduction of deep implicit 3D shape models for the female breast represents a significant leap in capturing detailed surface geometry without the need for complex registration processes. Overall, the field is moving towards more efficient, accurate, and practical solutions that leverage the power of neural networks and advanced data representations.