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NeRFPlayer: A Streamable Dynamic Scene Representation with Decomposed Neural Radiance Fields
Liangchen Song, Anpei Chen, Zhong Li, Zhang Chen, Lele Chen, Junsong Yuan, Yi Xu, Andreas Geiger
TL;DR
Dynamic-scene NeRF reconstruction is difficult with sparse camera observations and differing spatial-temporal frequencies. NeRFPlayer decomposes scenes into static, deforming, and new areas and streams hybrid feature channels; it achieves efficient reconstruction and interactive rendering with comparable or superior performance to recent methods.
Problem
Dynamic NeRFs require reconstruction from sparse observations while modeling scene appearance and geometry whose frequencies differ across spatial and temporal dimensions.
Method
NeRFPlayer uses a self-supervised decomposition field for static, deforming, and new areas, separate neural fields with regularization, and sliding-window hybrid feature streaming.
Results
The method achieves comparable or superior rendering quality and speed to recent state-of-the-art methods, reconstructing in 10 seconds per frame with interactive rendering.
Takeaways & Limitations
Experiments across single-camera and multi-camera datasets support the efficiency and effectiveness of decomposed, streamable dynamic-scene representation.
Takeaways & Limitations
The representation limits long-range repeated activities and assumes multi-view images share the same camera imaging configuration.
Abstract
from arXiv · showhide
Visually exploring in a real-world 4D spatiotemporal space freely in VR has been a long-term quest. The task is especially appealing when only a few or even single RGB cameras are used for capturing the dynamic scene. To this end, we present an efficient framework capable of fast reconstruction, compact modeling, and streamable rendering. First, we propose to decompose the 4D spatiotemporal space according to temporal characteristics. Points in the 4D space are associated with probabilities of belonging to three categories: static, deforming, and new areas. Each area is represented and regularized by a separate neural field. Second, we propose a hybrid representations based feature streaming scheme for efficiently modeling the neural fields. Our approach, coined NeRFPlayer, is evaluated on dynamic scenes captured by single hand-held cameras and multi-camera arrays, achieving comparable or superior rendering performance in terms of quality and speed comparable to recent state-of-the-art methods, achieving reconstruction in 10 seconds per frame and interactive rendering.
1. Introduction
NeRFPlayer addresses dynamic-scene reconstruction by decomposing areas according to temporal behavior and streaming hybrid feature representations for efficient modeling and rendering.
- Motivation: Dynamic NeRFs face sparser supervision and mismatched spatial-temporal frequencies, making temporal modeling and interpolation difficult.Adding time to NeRF requires handling fewer observations per spatiotemporal point and different scene variation frequencies across space and time.
- Method: The framework decomposes scenes into static, deforming, and new areas using a self-supervised decomposition field with global parsimony regularization.The field predicts point-wise category probabilities, while regularization discourages excessive modeling of areas as new.
- Method: Separate temporal regularizations for decomposed areas reduce reconstruction ambiguity from sparse observations and align temporal frequencies within each area.Static regions can use static-scene modeling, while deforming regions enforce dynamic consistency.
- Method: A sliding window over feature channels introduces time into hybrid spatial representations without constructing full 4D feature volumes.The scheme treats channels of spatial feature volumes as temporally dependent and supports streamable representation.
- Evaluation: Experiments and ablations cover single-camera and multi-camera datasets and assess the necessity of the three temporal patterns under different capture conditions.The studies examine all three areas for single-camera data, static decomposition for multi-camera data, and deformation modeling for large-motion scenes.
2. Related Work
Prior work spans neural-field representations, hybrid acceleration, scene decomposition, and free-viewpoint video, while dynamic rendering still involves tradeoffs among speed, size, and quality.
- Neural and Hybrid Representations: Neural fields represent scene properties from coordinates, while hybrid representations combine explicit structures with implicit decoders for faster querying and rendering.Examples include voxel grids, hash tables, and tensor decompositions paired with small MLPs.
- Scene Decomposition: Scene decomposition methods partition content by objects, spatial regions, semantics, motion, or static-versus-dynamic structure.NeRFPlayer instead focuses on temporal categories within dynamic scenes.
- Free-Viewpoint Video: Free-viewpoint video systems have used dense capture, tracked meshes, layered meshes, image-based rendering, and neural volumetric representations.These approaches support varied camera configurations and rendering goals, including streaming and view-dependent effects.
- Open Challenges: Dynamic-scene rendering remains open to further study for both single- and multi-camera inputs, especially regarding motion modeling and efficiency-quality tradeoffs.The paper positions its method around efficient, high-quality free-viewpoint rendering under both capture settings.
3. Preliminaries
NeRF renders ray colors by volume-accumulating sampled points, while hybrid representations improve efficiency by storing explicit features decoded by small neural networks.
- NeRF Rendering: NeRF samples points along each camera ray and computes pixel color through volume rendering from point densities and view-dependent colors.Points are sampled from the camera origin and direction, then accumulated to estimate the ray color.
- NeRF Rendering: The vanilla NeRF radiance field is an MLP mapping spatial points to density and color, trained with reconstruction loss against ground-truth ray colors.The loss is evaluated over a batch of ray samples.
- Hybrid Representations: Hybrid representations accelerate neural-field reconstruction by storing explicit features in voxel grids, hash tables, or basis vectors and decoding them implicitly.They retain differentiability while reducing the computational cost of purely implicit MLP representations.
- Framework Components: The framework overview combines streamed newness and decomposition fields with a stationary field, deformation field, and radiance field.The stationary field models time-invariant structure and low-frequency appearance changes, while deformation and radiance use small MLPs.
4. Our Method
NeRFPlayer decomposes dynamic scenes into areas with distinct temporal behavior and represents them with separate neural fields. A streamable hybrid representation and self-supervised regularization support compact, efficient spatiotemporal modeling.
- Decomposed Spatiotemporal Representation: The method assumes static, deforming, and new areas exhibit different temporal patterns and models each with a separate field.Static areas use a stationary field, deforming areas use a deformation field, and new areas use a newness feature field.
- Decomposed Spatiotemporal Representation: The decomposition field maps each spatiotemporal point to probabilities of being static, deforming, or new, which weight the corresponding feature vectors.The weighted feature vector is passed to a lightweight view-conditioned network for density and color prediction.
- Optimization: The decomposition is learned without manual probability labels using reconstruction loss and global parsimony regularization.The regularizer minimizes the probability of non-static points under an assumed static background and weights deforming versus new areas with α.
- Streamable Hybrid Representation: Feature-channel streaming extends hybrid representations to spatiotemporal fields without requiring a full 4D feature volume.A sliding window selects time-dependent feature channels, and channel rearrangement aligns shared channels across adjacent frames.
- Streamable Hybrid Representation: The sliding window reduces model size through overlapping channels and loads only new channels when rendering successive frames.The same representation also supports linear temporal interpolation between observed frames.
- Overall Framework: The overall framework combines cached explicit features with small MLP decoders, while using an MLP deformation field and streamed features for 4D inputs.The stationary field uses a tiny MLP for low-frequency, time-dependent appearance, while the decomposition and newness fields use explicit features.
5. Experiments
The experiments evaluate NeRFPlayer on single-camera and multi-camera dynamic-scene datasets using quantitative, qualitative, and ablation analyses. The study includes challenging single-view-per-timestamp data and examines rendering quality, speed, and motion-related performance.
- Evaluation: The evaluation compares prior methods quantitatively and qualitatively, including rendering comparisons on the Plenoptic Video dataset and fast-moving objects.The study also includes extensive ablations of the decomposition components and video frame rate.
- Datasets: Experiments use two multi-camera datasets and HyperNeRF, which provides only one view per timestamp in a challenging single-camera setting.The datasets cover synchronized camera arrays, indoor and outdoor scenes, and moving-camera or stereo capture configurations.
- Datasets: Immersive Video uses synchronized videos from 46 4K fisheye cameras, with seven selected dynamic scenes downsampled to 1280 × 960.The central camera is used for validation and the remaining cameras for training.
- Datasets: Plenoptic Video contains indoor activities captured by 21 cameras at 2704 × 2028 resolution and downsampled to 1352 × 1014 for experiments.Six scenes are publicly available, and the provided training-validation camera split is used.
- Implementation: The framework is implemented with InstantNGP and TensoRF backbones, using MLP-based deformation, stationary, and radiance fields.This implementation supports testing the proposed framework across two hybrid-representation backbones.
5.1. Comparison with State-of-The-Art Methods
NeRFPlayer is evaluated against state-of-the-art methods on multi-camera and single-camera dynamic-scene datasets, achieving strong reconstruction quality with substantially reduced rendering or training costs.
- NeRFPlayer reaches higher PSNR while significantly reducing training and rendering time compared with DyNeRF.With InstantNGP, it renders images in 12% of DyNeRF’s required time while remaining comparable in quality.
- NeRFPlayer with TensoRF-VM achieves better performance on fast-moving objects than the compared multi-camera results.The rendered knife is clearer than in DyNeRF’s results.
- On single-camera HyperNeRF-vrig, NeRFPlayer outperforms HyperNeRF in PSNR but is slightly worse in SSIM.The authors associate this with more accurate but less sharp images.
- NeRFPlayer’s decomposition regularizes deformation to dynamic areas, addressing moving-object misalignment and background flickering observed in comparisons.HyperNeRF sometimes misaligns moving objects and lacks static–dynamic decomposition.
5.2. Ablation Studies
Ablations show that temporal decomposition, regularization, bitrate, and backbone settings each affect reconstruction, streaming quality, and the speed–quality tradeoff.
- Impact of Decomposition: Removing new-area decomposition fails to model newly poured espresso, while removing deforming-area decomposition blurs the hand on a single-camera scene.Removing static-area decomposition also produces a flickering background at novel times and views.
- Impact of Decomposition: With low frame rates and larger motion, omitting deformation modeling makes the helmet disappear and reappear during interpolation.Modeling deformation preserves the helmet content and produces smoother interpolation.
- Scene Decomposition Regularizing: α = 1 produces blurry moving objects, whereas α = 0.005 produces a noisy scene.The authors recommend starting with a relatively large α and gradually decreasing it to allow deformation.
- Streaming Bitrates: TensoRF-CP provides low bitrate and reasonable performance, while TensoRF-VM costs more but delivers an obvious performance gain.Higher bitrate budgets reveal clearer background and moving-object details.
- Rendering Speed and Quality: The framework exposes a speed–quality tradeoff controlled mainly by backbone, predefined model size, and rendering hyperparameters.The parameters T and ray-marching step-ping affect rendering FPS and PSNR, allowing users to tune the desired balance.
6. Limitation and Failure Cases
The method supports streamable local-frame representations, but this design limits repeated-activity modeling and assumes consistent camera imaging configurations across input views.
- Local feature channels enable streaming but limit representation of long-range repeated activities.Modeling repeated activities could reduce redundancy by leveraging views of the same object.
- The method assumes multi-view images share the same camera imaging configuration, such as exposure.Under inconsistent configurations, it can overfit training views with floating points.
7. Conclusion
NeRFPlayer represents dynamic scenes from single- and multi-camera images using temporal decomposition and streamable hybrid feature representations, with experiments supporting its efficiency and effectiveness.
- The framework decomposes scenes into static, deforming, and new areas, then models them with a sliding-window hybrid representation.Experiments and ablations validate the framework and the need for deformation modeling in large-motion camera-array scenes.
Appendix
The appendix reports per-scene performance evaluations on the Immersive Video and Plenoptic Video datasets, with seven Immersive Video scenes identified as consistent among cameras.
- Per-scene performance evaluations are reported for the Immersive Video dataset.
- Per-scene performance evaluations are reported for the Plenoptic Video dataset.
- Table 5 marks seven Immersive Video scenes as consistent among cameras.
- Table 6 presents per-scene performance on the Plenoptic Video dataset.