DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes (2205.15723v2)

Abstract: Modeling dynamic scenes is important for many applications such as virtual reality and telepresence. Despite achieving unprecedented fidelity for novel view synthesis in dynamic scenes, existing methods based on Neural Radiance Fields (NeRF) suffer from slow convergence (i.e., model training time measured in days). In this paper, we present DeVRF, a novel representation to accelerate learning dynamic radiance fields. The core of DeVRF is to model both the 3D canonical space and 4D deformation field of a dynamic, non-rigid scene with explicit and discrete voxel-based representations. However, it is quite challenging to train such a representation which has a large number of model parameters, often resulting in overfitting issues. To overcome this challenge, we devise a novel static-to-dynamic learning paradigm together with a new data capture setup that is convenient to deploy in practice. This paradigm unlocks efficient learning of deformable radiance fields via utilizing the 3D volumetric canonical space learnt from multi-view static images to ease the learning of 4D voxel deformation field with only few-view dynamic sequences. To further improve the efficiency of our DeVRF and its synthesized novel view's quality, we conduct thorough explorations and identify a set of strategies. We evaluate DeVRF on both synthetic and real-world dynamic scenes with different types of deformation. Experiments demonstrate that DeVRF achieves two orders of magnitude speedup (100x faster) with on-par high-fidelity results compared to the previous state-of-the-art approaches. The code and dataset will be released in https://github.com/showlab/DeVRF.

• The paper introduces a novel method achieving a 100× speedup in training dynamic radiance fields using a voxel-based representation.
• It presents a two-stage static-to-dynamic learning paradigm that leverages a 3D canonical space for efficient deformation field training.
• The approach utilizes coarse-to-fine optimization, deformation cycle consistency, and regularization techniques to maintain high-fidelity view synthesis.

Fast Deformable Voxel Radiance Fields for Dynamic Scenes: An Overview of DeVRF

The paper "DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes" introduces a significant advancement in the field of dynamic novel view synthesis, specifically tackling the severe limitations of training times prevalent in Neural Radiance Field (NeRF)-based approaches. The authors propose DeVRF, a method that combines the efficiency of voxel-based representations with a novel static to dynamic learning paradigm, achieving an impressive two orders of magnitude speedup compared to the state-of-the-art (SOTA) methods without compromising on the high-fidelity of synthesized views.

Core Contributions and Methodology

1. Voxel-Based Representation: At the core of DeVRF is the use of explicit and discrete voxel grids to model both the 3D canonical space and the 4D deformation field in dynamic, non-rigid scenes. This approach allows for rapid querying of scene properties such as density and color, which is central to the method's efficiency.
2. Static to Dynamic Learning Paradigm: The authors introduce a two-step learning process where a 3D canonical space is first learned from multi-view static data. This learned canonical space acts as a strong geometric and appearance prior that facilitates the efficient training of a 4D deformation field using only a few views of dynamic sequences, significantly reducing the necessary computation time.
3. Optimization Strategies: To address potential overfitting due to the large number of parameters in the voxel representation, the paper proposes a coarse-to-fine training strategy. This incremental refinement approach smooths the optimization landscape. Additionally, the model leverages deformation cycle consistency, optical flow supervision, and total variation regularization to ensure accurate and efficient learning of dynamic scenes.

Numerical Results and Implications

The experimental results presented are notable. DeVRF not only achieves a 100× speedup in learning dynamic radiance fields but also consistently delivers on-par high-fidelity results, as demonstrated across both synthetic and real-world datasets. Such results underscore the practical implications of the method, suggesting that DeVRF is well-suited for real-time applications in virtual reality, telepresence, and 3D animation where rapid scene reconstruction is critical.

Theoretical and Practical Implications

The theoretical advancement in this work lies in successfully adapting techniques typically used for static scenes to the dynamic domain, shedding light on efficient representations and learning mechanisms for high-dimensional data. Practically, DeVRF's ability to function with limited dynamic views while leveraging static information reduces the cost and complexity of data capture, making it feasible for deployment in varied real-world settings.

Future Directions

While DeVRF represents a significant step forward, the discussion on limitations and future work in the paper points to avenues for further research. These include reducing the model size and memory footprint, accommodating more drastic deformations, and potentially optimizing the 3D canonical space in conjunction with dynamic scenes for enhanced performance.

In summary, DeVRF stands out as a robust solution for fast, high-fidelity novel view synthesis in dynamic scenes, offering both theoretical insights and practical benefits. Its introduction of voxel-based approaches into the dynamic domain may catalyze further innovations, expanding the capabilities and applications of radiance fields in computer vision.