Source-linked AI summary
HexPlane: A Fast Representation for Dynamic Scenes
Ang Cao, Justin Johnson
TL;DR
Dynamic scene reconstruction is challenging because real-world scenes are not static. HexPlane addresses this with an explicit six-plane representation, matching prior image quality while substantially reducing training time.
Problem
Real-world scenes are dynamic, making static-scene reconstruction assumptions restrictive for modeling and re-rendering 3D scenes.
Method
HexPlane explicitly represents dynamic 3D scenes with six feature planes that compute spacetime features through sampling and fusion, paired with a tiny MLP for color regression.
Results
HexPlane matches prior image quality on Plenoptic Video while improving training time by more than 100×, with a reported 672× speedup against DyNeRF.
Takeaways & Limitations
HexPlane provides a simple, explicit representation that is effective for dynamic novel view synthesis and robust across fusion, coordinate, and decoding choices.
Takeaways & Limitations
HexPlane is less effective than deformation fields for monocular videos, and synthesized results can contain color jittering and artifacts requiring stronger regularization.
Abstract
from arXiv · showhide
Modeling and re-rendering dynamic 3D scenes is a challenging task in 3D vision. Prior approaches build on NeRF and rely on implicit representations. This is slow since it requires many MLP evaluations, constraining real-world applications. We show that dynamic 3D scenes can be explicitly represented by six planes of learned features, leading to an elegant solution we call HexPlane. A HexPlane computes features for points in spacetime by fusing vectors extracted from each plane, which is highly efficient. Pairing a HexPlane with a tiny MLP to regress output colors and training via volume rendering gives impressive results for novel view synthesis on dynamic scenes, matching the image quality of prior work but reducing training time by more than $100\times$. Extensive ablations confirm our HexPlane design and show that it is robust to different feature fusion mechanisms, coordinate systems, and decoding mechanisms. HexPlane is a simple and effective solution for representing 4D volumes, and we hope they can broadly contribute to modeling spacetime for dynamic 3D scenes.
1. Introduction
Dynamic-scene reconstruction is difficult because implicit NeRF-based representations require costly MLP evaluations and dense 4D storage is infeasible. HexPlane addresses these constraints with six feature planes, achieving comparable quality with substantially faster training.
- Motivation: Implicit dynamic-scene methods require many MLP evaluations per pixel, making rendering and training expensive.Training can require up to days of GPU time for one scene.
- Motivation: Dense 4D grids are infeasible for large scenes or long durations because memory scales with the fourth power of grid resolution.The representation must also handle sparse observations.
- HexPlane: HexPlane decomposes a 4D spacetime grid into six feature planes and aggregates projected feature vectors before decoding colors with a tiny MLP.Novel views are rendered using volume rendering.
- HexPlane: Factored storage makes HexPlane memory scale quadratically with scene resolution, while independently tunable plane resolutions accommodate different spatial and temporal capacity needs.Spatial-only planes also encourage information sharing across disjoint timesteps.
- Results: On Plenoptic Video, HexPlane matches prior image quality while improving training time by >100×, and it outperforms prior approaches on a monocular video dataset.Ablations find robustness across feature fusion, coordinate, and decoding mechanisms.
- Results: HexPlane is presented as a simple, explicit, and general representation that avoids deformation fields and category-specific priors.The authors position it as applicable beyond dynamic-scene view synthesis.
2. Related Work
Prior work includes implicit neural representations and several explicit representations, but explicit methods had largely focused on static scenes. HexPlane extends explicit factorization to dynamic fields while avoiding time-aware MLPs for spacetime features.
- Neural Scene Representations: Neural representations have advanced novel-view synthesis and applications including reconstruction, segmentation, generation, and 3D content creation.NeRF and its variants are central examples of this line of work.
- Explicit Representations: Existing explicit representations include sparse voxels, low-rank components, and point clouds, but they generally assume static scenes.HexPlane provides an explicit model for dynamic scenes.
- Dynamic Neural Rendering: Dynamic NeRF methods add time dimensions, latent codes, deformation fields, or canonical fields, but some require additional supervision because the problem is under-constrained.Deformation and canonical-field approaches can learn from sparse monocular views.
- Accelerating NeRFs: Concurrent dynamic-NeRF methods remain partially implicit because time-aware MLPs obtain spacetime features, whereas HexPlane uses an explicit dynamic-field representation.This distinction concerns how spacetime features are represented.
- Related Factorizations: Tensor4D and D-TensoRF use alternative plane or tensor factorizations, while K-Planes is the closest related method because it also employs six feature planes.The comparison identifies representational proximity rather than an outcome ranking.
3. Method
The method represents dynamic 3D scenes explicitly with a factored spacetime structure, then decodes queried features for volumetric novel-view synthesis. HexPlane balances representational power and speed while addressing memory and sparse-observation challenges.
- HexPlane overview: HexPlane combines a new explicit representation with a small implicit MLP for dynamic-scene novel view synthesis.Spacetime points query explicit features, which the MLP combines with coordinates and view direction to regress RGB color.
- Design challenges: A naïve dynamic 4D volume requires O(N^3TF) space, and sparse views per timestep make independently modeling each timestep insufficient.For RGB colors at N=512 and T=32, the naïve volume requires 48GB in float32 format.
- Factorized temporal modeling: The representation factorizes spacetime into shared components so information can be shared across time, while reducing memory relative to independent per-timestep volumes.Shared basis volumes address temporal sharing, but the authors found that storing many such volumes remains too costly for GPU memory.
- HexPlane representation: HexPlane replaces fully separated spatial and temporal factors with joint coordinate-pair planes, allowing entangled space-time dependencies such as motion trajectories.The construction uses three spatial and three spatio-temporal plane pairs, including XY/ZT, XZ/YT, and YZ/XT.
- HexPlane representation: A HexPlane maps a spacetime point to an F-dimensional feature using elementwise products and vector-matrix products over learned feature planes.Its query requires six bilinear interpolations and a vector-matrix product, with memory scaling as O(N^2R + NTR + RF).
- Optimization: The model queries opacity and appearance features from HexPlane, predicts colors with a tiny MLP, and renders images through volumetric rendering optimized by photometric loss.A separate HexPlane can provide appearance features for points with high opacity, while an emptiness voxel skips empty regions for acceleration.
4. Experiments
Experiments evaluate HexPlane on challenging multi-camera and monocular dynamic-scene datasets, comparing quality, speed, and design robustness. HexPlane achieves competitive or superior synthesis with major efficiency gains and remains robust across factorization, coordinate, and resolution choices.
- Evaluation Settings: HexPlane evaluation covers high-resolution multi-camera Plenoptic Video and monocular D-NeRF datasets with distinct dynamic-scene challenges.Plenoptic Video tests complex content and fine details, while D-NeRF tests monocular videos and sparse observations.
- Dynamic Novel View Synthesis Results: Over 100× faster training with matched image quality is reported on Plenoptic Video compared with prior work.Table 1 reports quantitative synthesis metrics and training times relative to DyNeRF.
- Dynamic Novel View Synthesis Results: 672× speedups are achieved by HexPlane† while maintaining comparable quantitative results to DyNeRF on Plenoptic Video.The full-iteration HexPlane model is reported to outperform DyNeRF in all metrics except PSNR while remaining above 100× faster.
- Dynamic Novel View Synthesis Results: On D-NeRF, HexPlane achieves comparable or better results than deformation-based methods without deformation and is noticeably faster.The comparison targets monocular dynamic-scene reconstruction, where prior methods commonly use deformation and canonical fields.
- Ablations and Analysis: Ablations show that HexPlane’s feature-plane design, coordinate choices, and spacetime resolutions affect results differently, while the overall design remains robust.Spatial and spatial-temporal planes are both needed; higher spatial resolution improves synthesis, whereas smaller time-grid resolution has little visible effect, and spherical coordinates support unbounded scenes.
- View Synthesis Results on Real Captured Video: HexPlane produces realistic monocular-video synthesis without deformation or category-specific priors, modeling backgrounds, casual motion, topology changes, and fine details.The videos were captured by iPhone with relatively casual camera trajectories closer to real-world use cases.
5. Conclusion
HexPlane is proposed as a simple, general explicit representation for dynamic 3D scenes, using six feature planes to compute spacetime features through sampling and fusion. The authors report comparable or better dynamic novel-view synthesis quality with accelerations exceeding hundreds of times.
- HexPlane represents dynamic 3D scenes explicitly with six feature planes that compute spacetime features through sampling and fusion.
- HexPlane could achieve comparable or better dynamic novel-view synthesis quality than implicit representations while providing over hundreds of times acceleration.
- The design aims to remain neat and general by avoiding deformation fields, category-specific priors, and other scene-specific tricks.
6. General Discussions
The discussion positions HexPlane as a reconstruction and rendering method rather than a scene-generation system, while examining its practical scope and limitations. Its simplicity improves generality but can reduce performance in extremely sparse observations and leave artifacts or other quality gaps.
- General Discussions: HexPlane reconstructs existing scenes and renders alternate viewpoints and timesteps rather than generating new scenes or nonexistent content.
- General Discussions: The method is evaluated on the public Plenoptic Video dataset, which contains human faces in videos and is licensed CC-BY-NC 4.0.
- General Discussions: HexPlane uses hundreds of times less computation than existing methods, potentially reducing carbon emissions through lower GPU resource usage.
- General Discussions: The authors prioritize a simple, general representation with minimal scene assumptions rather than optimizing solely for state-of-the-art numbers.
- Limitations and Future Directions: Without deformation fields, HexPlane is less effective for scenes with extremely sparse observations, although it uses inherent basis sharing to address sparse views.
- Limitations and Future Directions: Category-specific priors such as 3DMM or SMPL can improve results substantially but are restricted to particular scenes.
- Limitations and Future Directions: HexPlane can produce color jittering and artifacts, motivating stronger regularization, optical-flow losses, separate foreground-background models, or clip-specific bases.
- Limitations and Future Directions: The authors identify dynamic scene generation and editing as potential applications beyond dynamic novel-view synthesis.
7. Training Details and More Results
The supplementary experiments describe dataset setups, training schedules, representation variants, fusion ablations, and qualitative results. They report that HexPlane supports multiple fusion mechanisms and that opacity regression with an MLP improves results at the cost of slower rendering.
- Training Details and More Results: The Plenoptic Video Dataset is a multi-view real-world video dataset with 10-second videos, with training and testing views shown in Figure 8.
- Training Details and More Results: Grid resolutions are adjusted according to axis boundary distances, assigning larger resolutions to spatial axes spanning longer ranges.
- Training Details and More Results: Training uses progressive spatial-grid upsampling, with dataset-specific initialization, optimization schedules, and hierarchical training stages.
- Training Details and More Results: HexPlane produces visually smoother results than HexPlane† on the reported scenes, although no baseline results are available for additional evaluation metrics.
- Training Details and More Results: Volume Basis, piece-wise temporal functions, VM-T, and CP decomposition are evaluated as alternative 4D-volume representations.
- Training Details and More Results: HexPlane is evaluated on per-scene results for the Plenoptic Video and D-NeRF datasets, with visualizations and depths reported in Figures 9 and 10.
- Training Details and More Results: Using an MLP to regress opacities substantially improves results across designs but slows rendering.
- Training Details and More Results: HexPlane supports varied feature-fusion mechanisms, demonstrating robustness across fusion designs on the D-NeRF dataset.
8. Failure Cases
HexPlane has identifiable failure cases when motion is too fast or observations are too sparse to synthesize scene details.
- HexPlane generates degraded results for objects moving too fast or scenes with too few observations to synthesize details.
9. Failed Designs for Dynamic Scenes
The authors evaluate alternative temporal and frequency-domain representations for dynamic scenes, finding that they incur substantial memory, speed, or quality problems relative to HexPlane. Tensor decomposition reduces one representation’s memory demands, but the resulting designs still produce distortions, slower computation, or degraded colors and details.
- Fixed Basis for Time Axis: A fixed-basis temporal representation stores feature matrices whose time-specific vectors are computed by combining basis coefficients with spatial feature coefficients.The authors use sine/cosine functions for the temporal basis and also test Legendre and Chebyshev polynomials.
- Fixed Basis for Time Axis: 7GB of storage and around 30GB during training are required for a 128^3 representation with Rt = 32 and F = 27.The implementation is also extremely slow because it repeatedly reads and writes memory values.
- Fixed Basis for Time Axis: Tensor decomposition factorizes the volumes into matrices and vectors, then uses f(t) to obtain the representations for the current time step before feature computation.This reduces memory usage compared with directly storing the full temporal feature representation.
- Fixed Basis for Time Axis: The fixed-basis design models dynamic scenes but suffers from severe color jittering and distortions, while its additional matrix-vector operation reduces speed relative to HexPlane.Weighted L1 regularization and bandwidth annealing are used to regularize high-frequency coefficients and gradually introduce them during training.
- Frequency-Domain Designs: A frequency-domain alternative decomposes 4D frequency volumes into vectors to reduce computation, replacing traversal of the full volume with summation along each axis.The method applies tensor decomposition because storing the frequency volume is memory-consuming.
- Frequency-Domain Designs: The frequency-domain design produces less-saturated colors and degraded details, while replacing HexPlane’s grid sampling with inner products leads to slower speeds.The authors also try wavelet series and other decompositions.