The recent advancements in 3D modeling and human-object interaction (HOI) have significantly pushed the boundaries of what is possible in virtual and augmented reality, robotics, and animation. Innovations in text-driven 3D HOI generation are now focusing on creating whole-body interactions that are not only realistic but also physically plausible, even in out-of-domain scenarios. This shift is enabled by the integration of advanced diffusion models and dynamic adaptation mechanisms that enhance the robustness and accuracy of generated interactions. Additionally, the development of specialized metrics for evaluating the quality of medical images and 3D object synthesis has provided new tools for ensuring the realism and anatomical consistency of generated content. These metrics, such as the Radiomic Feature Distance (RaD), are proving to be superior to traditional perceptual metrics in various applications, including out-of-domain detection and image-to-image translation. Furthermore, the field is witnessing a rise in the use of large language models and machine learning frameworks to facilitate the creation and retrieval of 3D objects, making the design process more efficient and accessible. These frameworks, like CLAS, are enabling the automatic retrieval of 3D objects based on user specifications, thereby unlocking the potential of existing datasets. The integration of 3D shape-aware prompts in text-to-image synthesis is another notable development, enhancing the consistency and diversity of generated images while maintaining a strong connection to the original 3D shape and textual context. This approach, exemplified by ShapeWords, is paving the way for more sophisticated and contextually accurate image synthesis. Lastly, the introduction of learnable metrics for evaluating the realism of human bodies in text-to-image generation, such as BodyMetric, is addressing the critical need for scalable and accurate evaluation methods, which are essential for benchmarking and improving text-to-image models.