The recent advancements in the field of 3D modeling and simulation for augmented and virtual reality (AR/VR) and robotics are pushing the boundaries of what can be achieved with automated and generative models. There is a notable shift towards leveraging vision-language models (VLMs) to create more interactive and realistic digital environments. This approach is not only enhancing the quality of simulations but also reducing the dependency on extensive human intervention, thereby making the process more scalable and efficient.

One of the key innovations is the development of systems that can automatically articulate complex objects from various input modalities, such as text, images, and videos. These systems are capable of generating code that can be compiled into interactable digital twins, significantly improving the success rates in tasks like robotic manipulation. Additionally, there is a growing focus on generating realistic human-object interactions, which is crucial for applications in AR/VR and robotics.

Another significant trend is the use of zero-shot text-driven deformation systems for 3D shapes, which allow for more intuitive and user-friendly control over the design process. These systems are particularly useful in the manufacturing sector, where they can streamline the creation of customized 3D models.

The integration of deep reinforcement learning (DRL) in combinatorial construction tasks is also gaining traction, enabling robots to complete incomplete assemblies autonomously. This development is crucial for advancing automation in industries that rely on precise and complex assembly processes.

Noteworthy papers include one that introduces a novel generative method for creating realistic human-object interaction scenes, and another that proposes a zero-shot text-driven 3D shape deformation system, both of which set new benchmarks in their respective areas.