Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video (2012.12247v4)

Abstract: We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes RGB images of a dynamic scene as input (e.g., from a monocular video recording), and creates a high-quality space-time geometry and appearance representation. We show that a single handheld consumer-grade camera is sufficient to synthesize sophisticated renderings of a dynamic scene from novel virtual camera views, e.g. a `bullet-time' video effect. NR-NeRF disentangles the dynamic scene into a canonical volume and its deformation. Scene deformation is implemented as ray bending, where straight rays are deformed non-rigidly. We also propose a novel rigidity network to better constrain rigid regions of the scene, leading to more stable results. The ray bending and rigidity network are trained without explicit supervision. Our formulation enables dense correspondence estimation across views and time, and compelling video editing applications such as motion exaggeration. Our code will be open sourced.

• The paper introduces a novel NR-NeRF framework that extends static NeRFs for dynamic, non-rigid scene reconstruction using ray bending and a rigidity network.
• It effectively disentangles scene geometry from deformation, enabling high-fidelity view synthesis from just a monocular video input.
• Extensive evaluations show improved metrics like PSNR, SSIM, and LPIPS over traditional methods, highlighting robust performance in dynamic scene rendering.

An Evaluation of Non-Rigid Neural Radiance Fields for Dynamic Scene Reconstruction and Novel View Synthesis

The paper under discussion introduces Non-Rigid Neural Radiance Fields (NR-NeRF), a methodological advancement aimed at reconstructing and synthesizing novel views of non-rigid, dynamic scenes using just monocular video footage. This approach leverages existing advancements in neural radiance fields (NeRF) to disentangle scene geometry and appearance from deformation, allowing for sophisticated rendering even from consumer-grade hardware.

Fundamental Approach

NR-NeRF extends the traditional NeRF framework, which assumes static scenes, by supporting dynamic, non-rigid deformable motion. For every input monocular sequence, a canonical neural radiance field representing the geometry and appearance is constructed. The scene’s dynamic nature is addressed through a deformation field, which bends rays instead of relying on straightforward directional estimations. This ray bending is managed by a neural network, essentially offering a flexible model to account for complex deformations relevant to the scene.

A notable improvement is the introduction of a rigidity network capable of distinguishing rigid from non-rigid components without requiring specific supervision. This enhancement aids in maintaining stable rendering of background elements, an aspect particularly challenging in traditional dynamic scene captures.

Innovations and Techniques

Multipronged innovations are pertinent to the performance of NR-NeRF:

1. Rigidity Network: This network is an unsupervised element integrated to organize portions of scenes into rigid and non-rigid elements, helping stabilize the visual fidelity of the scene.
2. Ray Bending: A unique approach solving inherently continuous deformations by leveraging a non-constrained parametrization, enabling synthesis beyond simple rigid transformations.
3. Loss Functions and Regularization: The formulation of losses that include magnitude, divergence, and rigidity components ensures a robust balance between preserving geometry and facilitating compelling deformation.
4. Latent Deformation Framework: By employing latent variables to represent deformations, NR-NeRF maintains a strong ability to synthesize novel views which traditional models struggle with.

Performance and Evaluation

The paper details an intensive evaluation process involving both qualitative and quantitative metrics:

• Qualitative Results: NR-NeRF is shown to outperform existing methods in rendering fidelity under the constraints of radically different views from the input paths.
• Quantitative Metrics: Across metrics like PSNR, SSIM, and LPIPS, NR-NeRF demonstrates improved performance, illustrating its ability to maintain detail and clarity in synthesized views.
• Comparative Analysis: Against prior works and baseline models, the NR-NeRF's design choices (e.g., ray bending and rigidity network) clearly outshine in preserving stability and authentic movement depiction in novel scenarios.

Implications and Future Prospects

The practical implications of NR-NeRF manifest prominently in fields such as virtual reality, augmented reality, and interactive media, where realistic rendering of dynamic environments is pivotal. The unsupervised learning of rigidity scores also signifies progress towards reducing reliance on extensive datasets for training, which often include diverse scenes and motion types.

The design offers new avenues for interacting with captured footage, such as motion exaggeration or removal of non-rigid objects, bringing creative tools to filmmakers and VR developers. The ability to handle general dynamic scenes lays a groundwork for further refinement, integrating elements such as subtle lighting changes and fine-grained surface dynamics.

Conclusion

NR-NeRF represents a substantive leap in computational graphics and neural rendering efforts. The shift towards dynamic scene rendering from monocular footage underpins many future developments in AI-assisted graphics, offering effective solutions to heritage problems in the domain of real-world renderings. The work's open-sourced code intentions further invite communal advancements and optimizations, underpinning a collaborative future for improved dynamic scene processing methodologies.