The field of acoustic modeling and virtual reality is rapidly advancing, with a focus on creating more immersive and realistic experiences. Researchers are exploring new methods for simulating sound propagation and modeling room impulse responses, which is essential for tasks such as speech dereverberation and virtual reality applications. One of the key directions in this field is the integration of physical and statistical modeling for room impulse response estimation. This approach enables the decomposition of impulse responses into interpretable parameters, allowing for more accurate and efficient modeling. Another area of research is the development of novel frameworks for simulating sound propagation in virtual environments. These frameworks utilize wave-based models and finite-difference time-domain methods to capture the complex phenomena of sound propagation, including occlusion, diffraction, reflection, and interference. The use of deep neural networks and machine learning techniques is also becoming increasingly popular in this field. Researchers are training models on synthetic datasets and evaluating their performance on real-world data, with impressive results. The development of new datasets and models is facilitating more flexible and perceptually driven room impulse response generation. Furthermore, the application of virtual reality technologies, such as Unreal Engine, is transforming the field of immersive storytelling and virtual production. In related areas, significant advancements are being made in generative modeling and physics-aware simulation. Latent diffusion models, variational autoencoders, and masked autoencoders are being used to improve the quality and diversity of generated images and videos. Additionally, there is a growing interest in incorporating physical properties and constraints into generative models, such as incompressibility and compressibility, to enable more realistic simulations. The field of computer vision is also witnessing significant developments in diffusion models and adversarial techniques. Researchers are actively exploring the limitations and potential of these models, including their performance with non-Gaussian noise and their ability to mitigate exposure bias. Overall, these advancements have the potential to revolutionize various fields, including computer vision, graphics, and engineering. Notable papers in these areas include proposals for novel approaches to room impulse response estimation, technical reviews of innovative virtual reality platforms, and introductions to new datasets and models for generative modeling and simulation.