Dynamic NeRF: A Review

Abstract: Neural Radiance Field(NeRF) is an novel implicit method to achieve the 3D reconstruction and representation with a high resolution. After the first research of NeRF is proposed, NeRF has gained a robust developing power and is booming in the 3D modeling, representation and reconstruction areas. However the first and most of the followed research projects based on NeRF is static, which are weak in the practical applications. Therefore, more researcher are interested and focused on the study of dynamic NeRF that is more feasible and useful in practical applications or situations. Compared with the static NeRF, implementing the Dynamic NeRF is more difficult and complex. But Dynamic is more potential in the future even is the basic of Editable NeRF. In this review, we made a detailed and abundant statement for the development and important implementation principles of Dynamci NeRF. The analysis of main principle and development of Dynamic NeRF is from 2021 to 2023, including the most of the Dynamic NeRF projects. What is more, with colorful and novel special designed figures and table, We also made a detailed comparison and analysis of different features of various of Dynamic. Besides, we analyzed and discussed the key methods to implement a Dynamic NeRF. The volume of the reference papers is large. The statements and comparisons are multidimensional. With a reading of this review, the whole development history and most of the main design method or principles of Dynamic NeRF can be easy understood and gained.

• The paper introduces dynamic NeRFs by adding a temporal input, enabling realistic modeling of deformable and moving scenes.
• The paper details methods like ray bending and skeleton-driven deformation to efficiently handle non-rigid objects and animate characters.
• The paper demonstrates significant speed improvements and versatility in applications from virtual reality to autonomous driving.

Making Dynamic NeRFs Understandable

Neural Radiance Fields (NeRFs) have revolutionized the world of 3D reconstruction and representation. But let's face it, the static nature of traditional NeRFs did put a limitation on practical applications. Enter Dynamic NeRFs, which aim to bring more flexibility and realism by incorporating motion into the mix. In this post, we're going to break down the key ideas from the paper "Dynamic NeRF: A Review."

What is Dynamic NeRF?

A Neural Radiance Field (NeRF) essentially uses a neural network to create 3D scenes from 2D images. It does this by training a model to predict color and density given a spatial location and viewing direction. The innovative twist in Dynamic NeRFs is the addition of motion. Static NeRFs capture a moment in time, but Dynamic NeRFs aim to represent moving scenes—think videos rather than snapshots.

Major Advances and Techniques

1. Adding Time as an Additional Input

One of the foundational methods for creating Dynamic NeRFs involves adding time as an additional input to the neural network. By treating time as another variable, the model can handle deformable objects and scenes.

• D-NeRF Example: In the D-NeRF model, time is an additional input alongside space and viewing direction. The network uses this to learn how the scene changes over time.
• What's Cool About It: This method is like giving the neural network a 'remember button'—it knows how things were and predicts how they will evolve.

2. Ray Bending for Non-Rigid Objects

Another interesting approach is using ray bending, which focuses on handling complex, non-rigid transformations such as those found in moving bodies or other dynamic elements of a scene.

• NR-NeRF Example: The NR-NeRF implement deformation by bending the rays of light that capture a scene, which allows the system to accommodate non-rigid, fluid movements without explicit supervision.
• What's Cool About It: Think Bullet Time in the Matrix—using ray bending, you can freeze time and manipulate the scene in amazing ways.

3. Skeleton-Driven Deformation

This method draws from animation techniques used in computer graphics. It's specifically useful for modeling human bodies and other complex structures that need to be animated.

• Skeleton-NeRF Example: This method uses a skeleton-driven approach to model dynamic scenes. It leverages neural blend weight fields and outputs multiple frames based on blend weights and skeleton motions.
• What's Cool About It: It makes 3D avatars and animated characters far more realistic and adaptable to movements.

Performance and Results

Dynamic NeRFs have shown strong numerical results in various domains:

• State-of-the-Art Performance: Methods like the HexPlane and Fast Dynamic NeRF have shown significant speed improvements, some even achieving 100x faster processing times.
• Impressive Versatility: Whether it's rendering large urban scenes or animatable human models, Dynamic NeRFs have proven versatile.

Practical and Theoretical Implications

Practical Applications:

1. Entertainment and Media: The gaming and film industry can greatly benefit from more realistic and dynamic 3D models.
2. Virtual Reality (VR): VR experiences can become more immersive with the ability to render dynamic environments and characters quickly and efficiently.
3. Autonomous Driving: Dynamic NeRFs can help in creating better 3D representations of the road, making the navigation systems more reliable.

Theoretical Insights:

1. Future of 3D Modeling: The addition of motion dynamics is a step towards creating more realistic 3D environments, bringing computer-generated imagery closer to real-world physics.
2. Advancements in Neural Networks: Techniques developed for Dynamic NeRFs could be adapted to other domains requiring dynamic data processing, such as weather modeling or biomechanics.

Looking Forward

Future developments in Dynamic NeRFs will likely focus on overcoming existing limitations such as:

• Editable NeRFs: One of the potential future directions could be making these models editable. This would open the doors to more interactive and user-driven applications.
• Improving Efficiency: While the processing times have been improving, making the NeRFs even more efficient can lead to real-time applications, which are immensely beneficial for gaming, simulations, and other interactive platforms.

Conclusion

The transition from static to dynamic NeRFs is poised to unlock new possibilities in 3D modeling and various practical applications. Whether it's enhancing the realism in VR games or creating detailed human avatars, Dynamic NeRFs represent a significant step forward. By incorporating time and motion, researchers are not just creating snapshots of reality—they're creating living, breathing digital worlds.