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Neural Radiance Flow for 4D View Synthesis and Video Processing

Yilun Du, Yinan Zhang, Hong-Xing Yu, Joshua B. Tenenbaum, Jiajun Wu

arXiv:2012.09790v2cs.CVcs.LGcs.RO

TL;DR

Learning photorealistic novel views of complex dynamic scenes from sparse observations remains challenging, particularly when only one camera is available. NeRFlow learns a spatiotemporal scene representation by aggregating observations across time with consistency constraints on appearance, density, and motion. It yields high-quality 4D view synthesis and outperforms a recent state-of-the-art method.

  • Problem

    Learning photorealistic novel views of complex dynamic scenes from sparse observations is challenging, including when only one camera is available.

    Source: §1 · p. 1

  • Method

    NeRFlow learns a coherent spatiotemporal scene representation by aggregating partial observations across time with consistency constraints on appearance, density, and motion.

    Source: §1 · pp. 1–2

  • Results

    NeRFlow produces high-quality 4D view synthesis and outperforms a recent state-of-the-art method, while its scene prior outperforms classical and internal-learning methods for video denoising and super-resolution.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The representation can function as a dynamic-scene prior for video denoising and super-resolution without additional supervision.

    Source: §1 · p. 2

    The method does not explicitly resolve ambiguities in 3D geometry and dynamic regions, complicating complex real scenes and preservation of static backgrounds over time.

    Source: §5 · p. 8

Abstract

from arXiv · show

We present a method, Neural Radiance Flow (NeRFlow),to learn a 4D spatial-temporal representation of a dynamic scene from a set of RGB images. Key to our approach is the use of a neural implicit representation that learns to capture the 3D occupancy, radiance, and dynamics of the scene. By enforcing consistency across different modalities, our representation enables multi-view rendering in diverse dynamic scenes, including water pouring, robotic interaction, and real images, outperforming state-of-the-art methods for spatial-temporal view synthesis. Our approach works even when inputs images are captured with only one camera. We further demonstrate that the learned representation can serve as an implicit scene prior, enabling video processing tasks such as image super-resolution and de-noising without any additional supervision.

Yilun Du MIT CSAIL

The passage identifies Joshua B. Tenenbaum as affiliated with MIT CSAIL, BCS, and CBMM.

1. Introduction

NeRFlow learns a dynamic scene representation from sparse observations, including as few as one camera, by enforcing temporal consistency across appearance, density, and motion. The representation supports 4D view synthesis and serves as a scene prior for video denoising and super-resolution without additional supervision.

1. Introduction

  • Traditional motion-capture approaches specialize in particular phenomena and struggle with complex occlusions and fine motion detail, while neural scene representations often assume static scenes and many cameras [42].High-resolution coverage also requires prohibitive memory.Source: §1 · p. 1
  • NeRFlow aggregates sparse observations across time to learn a coherent spatio-temporal scene representation, including from as few as one camera with known camera parameters.Temporal consistency constraints apply to scene appearance, density, and motion.Source: §1 · pp. 1–2
  • The method jointly learns a 6D radiance function for appearance and density and a 4D flow function for dynamics, with each module informing the other across time.Flow provides temporal correspondences that propagate appearance and density, while radiance-derived geometry informs motion; the model is fully differentiable.Source: §1 · pp. 1–2
  • NeRFlow produces high-quality 4D view synthesis, outperforming a recent state-of-the-art method, and improves video denoising and super-resolution over classical and state-of-the-art internal learning methods.The video-processing tasks require no additional supervision.Source: §1 · p. 2
  • The evaluation spans fluid pouring, an occluded robot moving over long distances, real scenes with transparent objects, and monocular videos of human motion.These setups test fluid dynamics, long-range motion, transparency, and monocular capture.Source: §1 · p. 2

2. Related Works

Related work spans neural implicit scene representations, 4D reconstruction, and spatial-temporal view synthesis, but prior approaches face limits in supervision, observations, scene scope, or temporal consistency. NeRFlow is presented as a continuous spatial-temporal radiance field learned from sparse observations, with applications to video processing.

Neural scene representations

  • Neural continuous implicit fields address discretization and resolution limits of voxel grids, point clouds, and meshes, while neural SDF and occupancy methods require ground-truth 3D supervision [51] [41].Differentiable rendering also connects image observations to 3D representations, enabling image-based optimization, including NeRF learning from calibrated images [48] [42].Source: §2 · p. 2
  • Prior differentiable-rendering and NeRF methods consider only static scenes, whereas NeRFlow targets dynamic scenes from a few moving cameras and limited observations.Aggregating sparse, partial observations across time is a central challenge in this setting.Source: §2 · p. 2

4D reconstruction

  • General-scene 4D reconstruction often needs full observations at every timestamp and omits appearance, while template-based methods cover specific domains but depend on template quality [45] [44] [24] [12] [27] [67].NeRFlow instead learns from limited observations without domain-specific assumptions.Source: §2 · p. 2

Novel view synthesis

  • Spatial-temporal view synthesis is less explored than spatial or temporal synthesis; prior warping-based methods [36] are resolution-restricted, unlike NeRFlow's continuous implicit representation.Other approaches extend temporal synthesis through temporal warping or frame-by-frame generation [36] [2].Source: §2 · p. 2

Deep networks as prior

  • NeRFlow's rendering architecture is proposed as an implicit dynamic-scene prior, and fitting noisy or low-resolution observations yields denoising and super-resolution.The learned radiance fields can take noisy or low-resolution images as input and render non-noisy or high-resolution images.Source: §2 · pp. 2–3

Concurrent Work

  • Unlike concurrent methods using timestamp-dependent deformation fields [52] [54], NeRFlow learns one continuous spatial-temporal radiance field and enforces continuous-time consistency with a neural ODE [8].This enables rendering across viewpoints and timestamps, including intermediate times; [77] [31] enforce consistency only at observed timestamps, and [77] reports poor interpolated renderings.Source: §2 · p. 3

3. Neural Radiance Flow (NeRFlow)

NeRFlow learns a spatio-temporally coherent representation of dynamic scenes from potentially limited posed image observations. It jointly models appearance, density, and motion, using consistency losses to propagate information across time.

3.1. Radiance and Flow Fields

  • The radiance field maps position, time, and view direction to density and diffuse/specular color, while a space-time flow field represents instantaneous motion that can be integrated to move points.Density is view-independent, and specular color receives L2 regularization because it is sparsely observed.Source: §3.1 · p. 3

3.2. Temporally Coherent Learning

  • Appearance and density consistency propagate diffuse color and preserve density along flow correspondences, while motion losses suppress flow in empty space and penalize flow gradients.The method relies on diffuse color constancy and constant object solidity; flow regularization reflects static empty space and smooth motion.Source: §3.2 · pp. 3–4

3.3. Learning from Visual Observation

  • NeRFlow trains radiance from posed RGB images and trains flow with 3D keypoint correspondences derived from optical flow, depth maps, and camera poses.It integrates the flow field to predict correspondence locations and minimizes their error against ground-truth correspondences.Source: §3.3 · p. 4

3.4. Implementation Details

  • Training warms up with rendering loss, then combines rendering, correspondence, appearance, density, and motion losses.The appearance and density loss weights are α, β = 0.001; positional embeddings are used for radiance inputs but omitted from flow inputs to encourage smooth predictions.Source: §3.4 · p. 5

4. Experiments

Experiments evaluate NeRFlow on synthetic and real dynamic scenes, testing 4D view synthesis, inferred depth and flow, and video denoising and super-resolution. The results show strong novel-view rendering across settings and use of the learned representation as a scene prior for video processing.

4.1. 4D View Synthesis

  • In Full View tests, NeRFlow outperforms baselines on every reported metric for Pouring and Gibson, capturing fluid dynamics and handling robot occlusions.The cited tables identify the evaluated datasets and comparison scope; no numerical cell values are supplied in the passages.Source: §4.1 · p. 5, Table 1 · p. 6, Table 2 · p. 6
  • In Stereo Views and Dual Views, NeRFlow again outperforms all baselines, with consistency enabling significantly better quantitative performance.These settings require synthesizing views across timestamps from two cameras rotating together; consistency helps integrate radiance information across timestamps.Source: §4.1 · pp. 5–6, Table 1 · p. 6, Table 2 · p. 6
  • With sparse timestamps, NeRFlow performs well at arbitrary times, and temporal consistency enables smooth renderings of intermediate timestamps.Training uses 1 of every 10 timestamps for Pouring and 1 of every 5 for Gibson; NonRigid NeRF does not apply because it learns per-timestamp latents.Source: §4.1 · p. 6, Table 1 · p. 6, Table 2 · p. 6
  • On real images, NeRFlow captures transparency and lighting effects where X-Fields struggles with ghosting, and consistently improves 4D synthesis over X-Fields on monocular videos.The video comparisons include unseen combinations of views and timestamps; the cited figure shows comparisons on real monocular video.Source: §4.1 · p. 6, Figure 6 · p. 6, Figure 7 · p. 7

4.2. Analysis and Visualization

  • NeRFlow’s inferred flow captures liquid and cup motion, while geometric consistency across time improves depth estimates, especially with limited cameras.The ablations describe consistency as separating static appearance and density from dynamic radiance: flow and motion consistency enforce near-zero flow at static locations, while nonzero flow propagates radiance for dynamic modeling; reducing either component harms the corresponding structures or regions.Source: §4.2 · p. 7, Figure 8 · p. 7, Figure 9 · p. 8

4.3. Video Processing

  • For video denoising and super-resolution, NeRFlow produces more realistic results than baselines; denoising also lowers reconstruction error, and super-resolution renders higher-resolution detail from low-resolution training images.On real monocular video, denoising achieves lower MSE and more realistic images as assessed by LPIPS; for super-resolution, Blind Video Prior achieves comparable image MSE.Source: §4.3 · pp. 7–8, Figure 10 · p. 8, Table 4 · p. 8

5. Discussion

NeRFlow learns a spatial-temporal representation for 4D view synthesis from limited camera observations and as a scene prior for video denoising and super-resolution. It leaves geometry and dynamic-region ambiguities unresolved, which can hinder complex-scene modeling and static-background preservation.

Discussion

  • NeRFlow supports 4D view synthesis from limited cameras, including monocular videos, and serves as a learned scene prior for video denoising and super-resolution.The method learns a spatial-temporal representation of dynamic scenes.Source: §5 · p. 8
  • Unresolved ambiguities in 3D geometry and dynamic regions hinder modeling complex real scenes and preserving static backgrounds over time.The approach does not explicitly address either ambiguity.Source: §5 · p. 8
  • Future work could separate static backgrounds from dynamic foregrounds to determine where flows are nonzero and use dense depth maps to resolve geometry ambiguity.Source: §5 · p. 8

Appendix: Neural Radiance Flow for 4D View Synthesis and Video Processing

The appendix provides supplementary visualizations, training details, and pseudocode for the overall algorithm, and directs reviewers to attached videos for qualitative examples.

A. Additional Visualizations

The visualizations show NeRFlow rendering dynamic scenes, inferring depth, interpolating across sparse timestamps, and supporting view synthesis and video processing. Comparisons and ablations illustrate how its renderings capture scene dynamics and structure.

Real Image View Synthesis

Real Depth Maps

Image Interpolations

  • Trained on one in five pouring-scene frames, NeRFlow consistently models liquid-drop volume across the animation when rendering all frames.The visualizations show intermediate timestamps, where consistency makes pouring volume more stable.Source: A. Additional Visualizations · p. 13, Figure 15 · p. 14

Full View Synthesis

Ablation Visualizations

Supplemental Visualizations

  • Supplemental videos show monocular 4D view synthesis, flow integration across time, free and multi-camera view synthesis, and video de-noising.Examples include stereo and dual-camera configurations on Pouring and Gibson scenes.Source: A. Additional Visualizations · p. 13

B. Additional Experimental Details

NeRFlow uses a two-stage training schedule and specific numerical settings for flow integration and loss weighting. The paper also provides pseudocode for training and sampling.

B. Additional Experimental Details

  • Models use the base architecture of [42] for radiance and flow, training for 10 hours with LRender and 10 more with all losses on one Nvidia 2080 Ti.Training uses PyTorch [53], Adam [29], a learning rate of 0.001, and exponential decay by a factor of 0.1 every 40,000 steps.Source: B. Additional Experimental Details · p. 14
  • Flow integration uses the Neural ODE library [8] with Runge–Kutta, RTOL = 10−4 and ATOL = 10−5; smaller tolerances slow training and produce less smooth flow fields.The authors report these higher-than-typical tolerances are critical for stable flow inference; loss coefficients are 0.1 for the L2 penalty on cspecular and 0.001 each for LCorr and LDensity.Source: B. Additional Experimental Details · p. 14
  • The paper provides Algorithm 1 as pseudocode for NeRFlow training and sampling.Its described stages include training and rendering from a chosen viewpoint and timestamp.Source: B. Additional Experimental Details · p. 14

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