Source-linked AI summary
Neural Volumes: Learning Dynamic Renderable Volumes from Images
Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, Yaser Sheikh
TL;DR
Dynamic scenes contain complex phenomena that challenge mesh-based reconstruction and tracking. Neural Volumes learns a renderable 3D volume from multi-view images with an encoder-decoder and differentiable ray marching, supporting novel viewpoints and content generation while combining warping and surfaces for fidelity.
Problem
Complex thin structures, translucency, occlusion, and biological motion make dynamic scenes difficult to reconstruct and track with high fidelity.
Method
An encoder-decoder learns a semi-transparent 3D volume from multi-view RGB images, and differentiable ray marching renders the volume end-to-end.
Results
The representation generalizes to novel viewpoints, generates novel sequences through latent traversal, and achieves higher fidelity through a learned warp field and optional surface integration.
Takeaways & Limitations
The resulting object- and scene-agnostic models support image-supervised dynamic reconstruction, novel content generation, real-time rendering, and interactive adjustments.
Takeaways & Limitations
An implicit MLP volume decoder requires prohibitive model size and per-ray evaluation for high-quality real-time applications.
Abstract
from arXiv · showhide
Modeling and rendering of dynamic scenes is challenging, as natural scenes often contain complex phenomena such as thin structures, evolving topology, translucency, scattering, occlusion, and biological motion. Mesh-based reconstruction and tracking often fail in these cases, and other approaches (e.g., light field video) typically rely on constrained viewing conditions, which limit interactivity. We circumvent these difficulties by presenting a learning-based approach to representing dynamic objects inspired by the integral projection model used in tomographic imaging. The approach is supervised directly from 2D images in a multi-view capture setting and does not require explicit reconstruction or tracking of the object. Our method has two primary components: an encoder-decoder network that transforms input images into a 3D volume representation, and a differentiable ray-marching operation that enables end-to-end training. By virtue of its 3D representation, our construction extrapolates better to novel viewpoints compared to screen-space rendering techniques. The encoder-decoder architecture learns a latent representation of a dynamic scene that enables us to produce novel content sequences not seen during training. To overcome memory limitations of voxel-based representations, we learn a dynamic irregular grid structure implemented with a warp field during ray-marching. This structure greatly improves the apparent resolution and reduces grid-like artifacts and jagged motion. Finally, we demonstrate how to incorporate surface-based representations into our volumetric-learning framework for applications where the highest resolution is required, using facial performance capture as a case in point.
1 INTRODUCTION
The paper introduces a learned volumetric representation for dynamic scenes that avoids explicit tracking and reconstruction while improving viewpoint generalization. Its encoder-decoder, latent scene representation, warp field, and hybrid surface integration support novel content and interactive rendering.
- Motivation: Complex occlusions, materials, motion, and other natural phenomena make high-fidelity reconstruction and tracking difficult for data-driven interactive rendering.Mesh-based methods depend heavily on reconstruction quality and are ill-suited to these cases.
- Motivation: A semi-transparent volumetric representation jointly learns geometry and appearance while dispersing ray-based gradients, widening convergence toward good solutions.The volume stores opacity and color at each 3D position and uses integral projection for rendering.
- Contributions: The decoder encourages viewpoint-generalizable solutions, while the encoder produces a low-dimensional latent scene state for jointly reconstructing sequences.Traversing the latent space enables modified recordings and new sequence animations without object-specific solutions.
- Contributions: A warp field addresses voxel memory and resolution limits, producing higher fidelity than a conventional voxel structure and enabling combination with surface-based reconstruction.The hybrid approach preserves a complete scene model while using surface methods where they work well.
- Contributions: The proposed representation is object- and scene-agnostic, uses image-level supervision, generalizes to novel viewpoints, jointly models dynamics, and supports end-to-end training.The resulting models support real-time rendering and on-the-fly adjustments for interactive VR applications.
2 RELATED WORK
The paper situates its method among volumetric reconstruction, ray-potential, deformable-volume, neural-rendering, and novel-view-synthesis approaches. It emphasizes explicit image formation, dynamic sequences, and generative latent representations without requiring precise geometric preprocessing.
- Overview: The approach spans volumetric reconstruction, deformable volumes, neural rendering, and novel view synthesis while combining learning with rendering.The related-work discussion compares similarities and differences across these areas.
- Volumetric Reconstruction: Classical voxel methods recover occupancy and color from multi-view photo-consistency, whereas this method learns a renderable volume directly.Its relationship to voxel-based reconstruction is described as similar in spirit.
- Ray Potentials: Unlike prior ray-potential approaches based on occupancy probability, the method models voxel transparency to render semi-transparent dynamic materials such as smoke.The explicit image-formation process supports rendering as well as reconstruction.
- Deformable Volumes: The warp-field renderer samples a 3D warp field first, then uses the resulting template-grid locations to sample color and opacity.This extends classical volume ray marching with learned spatial indexing.
- Novel View Synthesis: Unlike many novel-view methods, the system operates on sequences, creates an animatable model, and generates new embedding sequences within one framework.It avoids relying on a separate geometric proxy for each dynamic frame.
3 OVERVIEW
The method is an end-to-end autoencoder whose decoder produces a 3D RGB-opacity volume and whose fixed differentiable ray-marching layer renders images from camera parameters. Semi-transparency supports both learnable gradients and translucent thin structures.
- Pipeline: An encoder-decoder converts input images into a 3D volume, followed by differentiable ray marching that renders an image from camera parameters.The rendering operation is the autoencoder’s fixed-function final layer.
- Volume Representation: The volume maps each 3D position x to local RGB color and differential opacity.Opacity values are nonnegative, with 0 representing full transparency.
- Volume Representation: Semi-transparent volumes soften discrete representations so gradients can flow during learning and represent translucent objects or thin structures such as hair.The same representation therefore serves optimization and appearance modeling.
- Pipeline: For each time instant, synchronized calibrated multi-view images are encoded into a latent state, decoded into V(x; z), and rendered through accumulative ray marching.The system is trained end-to-end from the rendered output.
4 ENCODER NETWORK
The encoder maps subsets of multi-camera images into a consistent latent scene representation, optionally combined with conditioning variables before decoding. A variational bottleneck encourages smooth latent traversal and controllable dynamic content.
- Latent Representation: A consistent 256-dimensional latent representation z encodes the scene state and can be traversed to generate novel volume sequences rendered from any viewpoint.Conditional decoding can modify only part of the scene state when paired conditioning samples are available.
- Encoder Architecture: Each camera view passes through a dedicated CNN branch before multi-view features are concatenated and further encoded.The architecture is designed for inputs from a subset of the capture cameras.
- Encoder Architecture: Using K = 3 roughly orthogonal views worked well in practice while reducing memory and computational cost.The implementation used frontal, leftmost, and rightmost views with images downsampled to 334 × 512 pixels.
- Variational Encoding: A variational encoder outputs a diagonal 256-dimensional Gaussian whose KL-divergence from a standard Normal regularizes the latent space.This encourages smooth traversal between training samples.
- Conditioning: The variational bottleneck projects conditioning information out of latent dimensions, encouraging the decoder to use the conditioning variable during reconstruction.View conditioning can represent effects such as specularity for VR rendering.
5 VOLUME DECODERS
The volume decoders represent dynamic RGBα volumes from latent scene states using voxel grids or implicit networks, with warping fields improving effective resolution and motion modeling. View conditioning adds data-driven modeling of view-dependent appearance.
- 5 VOLUME DECODERS: Volume decoders parameterize RGBα volume functions from a scene state, using voxel grids, multilayer perceptrons, and warping fields.The section compares explicit voxel-grid and implicit MLP representations and introduces warping to improve effective resolution.
- 5.1 MLP Decoders: MLP decoders avoid voxel-grid resolution and storage limits but require prohibitive model sizes and repeated ray-wise evaluation for high-quality real-time reconstruction.The MLP must be evaluated at every ray-marching step, restricting both complexity and real-time use.
- 5.2 Voxel Grid Decoders: Voxel-grid decoders produce explicit 3D tensors sampled by trilinear interpolation, with neural networks generating tensors of size 4×D×D×D.The decoder is evaluated only inside its bounded volume, determined through volume-ray intersections.
- 5.3 Warping Fields: Warping fields decode a template RGBα volume and inverse warp, mapping output points to template locations while modeling motion and increasing effective voxel resolution.Inverse warps can represent output-space detail at higher resolution without additional memory and remain defined throughout the output space.
- 5.4 Mixture of Affine Warps: A spatial mixture of affine warps replaces overly flexible freely varying deconvolutional fields, because unconstrained fields overfit and generalize poorly to novel views.The mixture uses affine transformations whose parameters include rotation, scaling, and translation, with warped spatial mixture weights.
- 5.5 View Conditioning: View-conditioned RGB decoding uses normalized camera direction to model view-dependent appearance, while RGB and α branches remain separate.This enables data-driven modeling of effects such as specularities without imposing a fixed functional form.
6 ACCUMULATIVE RAY MARCHING
The renderer marches camera rays through semi-transparent volumes, accumulating color and opacity with front-to-back blending. Its differentiable numerical integration supports end-to-end optimization, while hybrid mesh rendering reserves volume capacity for areas needing it.
- 6.1 Semi-Transparent Volume Rendering: Semi-transparent volume rendering accumulates color according to local color and density as each camera ray traverses inhomogeneous material.The formulation mimics front-to-back additive blending.
- 6.1 Semi-Transparent Volume Rendering: Ray marching accumulates both color and opacity, stopping when opacity reaches 1 or the ray exits the volume to model occlusions.The ray segment is restricted to the interval intersecting the modeled volume.
- 6.1 Semi-Transparent Volume Rendering: The rendering algorithm numerically integrates ray color and opacity with the rectangle rule, and its differentiability enables optimizing volume parameters against target images.The implementation uses a step size of 1/128 the volume size and reports 90Hz rendering in an OpenGL shader.
- 6.2 Hybrid Rendering: Hybrid rendering terminates rays at intersected mesh depths and fills remaining color throughput with mesh color at the surface.This combines volumetric rendering with existing mesh-based representations for high-resolution surface detail.
- 6.2 Hybrid Rendering: During learning, hybrid rendering lets the volume avoid spending representational capacity where the mesh provides higher-fidelity detail, preventing it from occluding the mesh.The learned semi-transparent volume naturally avoids mesh occlusion in those regions.
7 END-TO-END TRAINING
End-to-end training combines camera calibration, background estimation, reconstruction priors, and a composite objective to improve volumetric reconstruction. These components reduce background and smoke-like artifacts while evaluating warping strategies for dynamic scenes.
- Training setup: Per-camera, per-channel gains and biases compensate for intensity differences between cameras before comparing reconstructions with ground truth.The calibration accounts for slight overall-intensity differences across views.
- Background estimation: Static per-camera background images are estimated and merged with ray-marched renderings using the remaining opacity outside the reconstructed volume.The background is static across each sequence and represents stationary objects generally outside the reconstruction volume.
- Background estimation: Background estimation greatly reduces reconstruction artifacts.
- Reconstruction priors: Without priors, calibration errors and view-dependent effects produce smoke-like artifacts by encouraging view-specific opacity.Two priors are introduced to reduce these artifacts.
- Reconstruction priors: Log-space total-variation regularization promotes sparse spatial gradients and sharp boundaries between opaque and transparent regions.The log formulation increases sensitivity to small opacity values because the artifacts are mostly transparent.
- Training objective: The training objective combines KL divergence, total-variation regularization, and beta-distribution regularization, optimized with Adam using fixed loss weights and learning rate 10^-4.The beta prior reduces exit-opacity entropy, reflecting that most rays should hit the object or background rather than graze the surface.
8 EXPERIMENTS
Experiments evaluate viewpoint generalization, warping choices, priors, qualitative reconstruction, temporal volume behavior, latent animation, and hybrid mesh-volume rendering across challenging dynamic scenes.
- Warping evaluation: The proposed affine mixture warp outperforms no-warp and convolutional-warp variants on moving hand, swinging hair, and dry ice smoke validation views.Convolutional warp fields completely fail on the hair-swing dataset, and computing mixture weights in warp space outperforms world-space weighting.
- View conditioning: View conditioning improves novel-view reconstruction by modeling view-dependent appearance and avoiding extra semi-transparent surface voxels.Table 1 compares view-conditioned and non-view-conditioned models on a human face.
- Priors and backgrounds: Priors improve validation performance across background settings, while a learned background with priors slightly outperforms a known background on the fuzzy toy.Table 2 reports training and validation MSE with and without priors and with known, learned, or absent background models.
- Qualitative results: Held-out renderings model fuzz, smoke, skin, and hair, but may add light smoky artifacts; more camera views tend to reduce artifacts.The qualitative results compare ground truth, reconstructions, and per-pixel root-mean-squared error on three datasets and three held-out viewpoints.
- Dynamic volumes: Warping explains much facial motion in template space, but limited resolution or complex warps can make motion appear as changing templates instead.The learned volumes also represent translucent glasses and coarse-resolution hair with lower opacity while retaining object-specific structure.
- Animation and hybrid rendering: Latent-code interpolation and user-conditioned decoding generate novel sequences, while hybrid mesh-volume rendering combines mesh detail for skin and eyes with volumetric hair modeling.The animation experiments include real-time avatar driving; the hybrid experiment uses a textured Deep Appearance Model mesh.
9 DISCUSSION
The method reconstructs challenging dynamic objects with semi-transparent volumes and supports real-time rendering, but its behavior has clear scope boundaries. These include limited-texture surfaces, refractive or high-frequency specular content, unconstrained latent-space traversal, and volumetric resolution limits.
- Results: The method convincingly reconstructs challenging objects such as moving hair, fuzzy toys, and smoke from multi-view RGB images without explicit tracking.It can also run in real time alongside traditional triangle rasterization.
- Limitations: Limited-texture surfaces may be represented as transparent, with their color placed in the background when occlusion is preserved.The resulting reconstruction degrades gracefully because training uses an image-space loss.
- Limitations: A depth-map example shows rays passing through the chest and terminating at the background because the chest resembles the background and has little texture variation.This artifact does not greatly affect reconstruction error, even from novel viewpoints.
- Limitations: Capturing sequences against a bright background, such as a green screen, is a practical way to address the limited-texture surface artifact.
- Limitations: The method handles transparent objects but does not currently model refractive surfaces, and high-frequency specular highlights are not represented correctly.Dull specular highlights can be represented through view conditioning.
- Limitations: Latent-space traversal without temporal guidance can generate visually accurate sequences that do not correspond to real object behaviors.The model does not explicitly represent temporal dynamics.
- Limitations: Volumetric representations remain resolution-limited because memory requirements grow cubically with voxel-grid resolution.Warping fields increase effective resolution without simply increasing voxel-grid resolution.