Neural 3D Video Synthesis from Multi-view Video (2103.02597v2)

Abstract: We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation. Our approach takes the high quality and compactness of static neural radiance fields in a new direction: to a model-free, dynamic setting. At the core of our approach is a novel time-conditioned neural radiance field that represents scene dynamics using a set of compact latent codes. We are able to significantly boost the training speed and perceptual quality of the generated imagery by a novel hierarchical training scheme in combination with ray importance sampling. Our learned representation is highly compact and able to represent a 10 second 30 FPS multiview video recording by 18 cameras with a model size of only 28MB. We demonstrate that our method can render high-fidelity wide-angle novel views at over 1K resolution, even for complex and dynamic scenes. We perform an extensive qualitative and quantitative evaluation that shows that our approach outperforms the state of the art. Project website: https://neural-3d-video.github.io/.

• The paper introduces DyNeRF, a dynamic neural radiance field that uses time-conditioned latent codes to represent both geometry and appearance changes in dynamic scenes.
• It employs a hierarchical coarse-to-fine training strategy with importance sampling, significantly reducing training time and computational costs.
• DyNeRF achieves a compact 28MB model for a 10-second 30 FPS video from 18 cameras, surpassing state-of-the-art methods in visual quality and performance metrics.

Neural 3D Video Synthesis from Multi-view Video

This paper presents an advanced approach to 3D video synthesis, proposing a dynamic neural radiance field model named DyNeRF, which is tailored for representing and rendering high-quality 3D video from multi-view recordings of complex, dynamic scenes. The method builds on the neural radiance field (NeRF) framework, extending it beyond static scenes by introducing time-dependent latent codes, enabling the efficient representation and synthesis of scene dynamics.

Core Contributions

1. Dynamic Neural Radiance Fields: The approach introduces a time-conditioned neural radiance field that employs a compact set of latent codes for capturing dynamic scene variations, both in geometry and appearance. These latent codes are learned from the input multi-view videos, allowing for a model-free and expressive 6D plenoptic function representation that encompasses both position, view direction, and temporal dynamics.
2. Efficient Training Methodology: The method significantly expedites training and enhances perceptual quality via hierarchical training and novel importance sampling strategies. The hierarchical approach begins with a coarse-to-fine training scheme using keyframes that capture salient features, while importance sampling prioritizes temporally salient pixels to accelerate learning. This results in faster convergence and reduces the computational resources required.
3. Compact Representation: DyNeRF achieves a remarkably compact model, capable of representing a 10-second 30 FPS video recorded from 18 cameras in just 28MB. This compactness does not compromise the quality, as the method successfully renders high-fidelity, wide-angle novel views at over 1K resolution.
4. Performance Advancements: The authors conduct extensive evaluations, demonstrating that DyNeRF surpasses existing state-of-the-art approaches, such as frame-by-frame NeRF and Neural Volumes, in terms of both visual quality and performance metrics like PSNR, SSIM, and LPIPS. The model provides substantial improvements in training speed—achieving results with an order-of-magnitude reduction in GPU hours compared to frame-by-frame NeRF.

Implications and Future Directions

The proposed DyNeRF method opens new avenues for practical applications in areas such as virtual and augmented reality, where high-quality rendering of dynamic real-world scenes is crucial. The ability to produce photorealistic interpolations and novel views in a computationally efficient manner is particularly advantageous for immersive digital media and telepresence systems.

Moreover, the paper highlights an opportunity to further optimize neural scene representations by integrating more sophisticated temporal supervision or motion models. Future work could explore generalizing the current framework to handle broader scenarios, including scenes with more rapid motion, or expanding its applications to autonomous driving and robotics, where real-time scene understanding is essential.

In sum, this research establishes a robust foundation for further explorations in dynamic scene synthesis, advocating for more efficient, compact, and scalable neural rendering techniques. As advancements continue in neural scene representation, methods like DyNeRF hold promise for increasingly realistic and interactive virtual experiences.