The recent advancements in dynamic scene reconstruction and real-time rendering have seen significant innovations, particularly in the use of 3D and 4D Gaussian Splatting techniques. Researchers are focusing on improving the efficiency and quality of dynamic scene representations, addressing challenges such as motion blur, temporal complexity, and memory constraints. Notable developments include methods for handling blurry monocular videos, compressing Gaussian representations for real-time applications, and optimizing for complex temporal dynamics. These approaches not only enhance visual fidelity but also enable real-time rendering on various devices, making them suitable for AR/VR and gaming applications. Additionally, there is a growing emphasis on adaptive optimization strategies and hierarchical representations to manage long volumetric videos efficiently. These advancements collectively push the boundaries of what is possible in dynamic scene synthesis and rendering, paving the way for more immersive and interactive experiences.

Noteworthy Papers:

• Deblur4DGS: Introduces the first 4D Gaussian Splatting framework for high-quality reconstruction from blurry monocular videos.
• TC3DGS: Achieves substantial compression of dynamic 3D Gaussian representations with minimal quality loss, suitable for real-time applications.
• SaRO-GS: Proposes a novel dynamic scene representation with real-time rendering capabilities and adaptive optimization for handling temporal complexities.