Source-linked AI summary
Temporal Residual Neural Radiance Fields for Monocular Video Dynamic Human Body Reconstruction
Tianle Du, Jie Wang, Xiaolong Xie, Wei Li, Pengxiang Su, Jie Liu
TL;DR
Dynamic human reconstruction with NeRF is constrained by MLP capacity, computation, and training time. The paper introduces TRes-NeRF with temporal residuals and rendering accelerations, reporting improved reconstruction metrics and nearly 780-fold higher time efficiency while maintaining similar accuracy to Anim-NeRF and Neural Body.
Problem
NeRF variants require many deep-MLP queries and lengthy training, limiting efficient reconstruction of large spatio-temporal signals such as long videos and dynamic 3D scenes.
Method
TRes-NeRF introduces temporal residuals and time-dependent layers, while factor decomposition and accelerated volumetric rendering reduce trainable parameters and computation.
Results
The method improves PSNR and SSIM over representative methods, maintains similar accuracy to Anim-NeRF and Neural Body, and achieves nearly 780-fold higher time efficiency.
Takeaways & Limitations
Temporal residuals improve reconstruction quality while reducing GPU memory, and factor decomposition accelerates both training and rendering.
Takeaways & Limitations
The method may not show advantages in challenging scenarios involving a lack of unsupervised constraints.
Abstract
from arXiv · showhide
In the field of computer vision and graphics, high-quality reconstruction of the human body in static scenes has been achieved in recent years by a single multilayer perceptron (MLP) in a number of approaches. However, MLPs have capacity limitations, requiring substantial training time and computational resources for dynamic scene reconstruction. And the quality of reconstruction is significantly constrained. This paper proposes a method for effectively processing complex spatiotemporal signals in dynamic scene human 3D modeling. The proposed method uses Temporal Residual Neural Radiance Fields to achieve novel view rendering and new pose synthesis of human bodies.To address the problem of representing temporal signals in video sequences, we construct a temporal residual field which is not related to the MLP architecture. Secondly, to improve reconstruction efficiency, we propose an integrated approach that reduces trainable parameters and accelerates rendering, thereby enhancing the network's feature representation capability. Finally, we design a multi-dimensional loss function to accurately measure the loss between predicted and actual spatial pixel values. The experimental results show that our proposed approach improves the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) accuracy metrics compared to the latest representative methods. It maintains similar accuracy to Anim-NeRF and Neural Body while achieving a nearly 780-fold increase in time efficiency.
1 INTRODUCTION
The paper targets slow, resource-intensive dynamic human reconstruction with TRes-NeRF, a temporal residual field and integrated rendering accelerations for monocular videos.
- NeRF-based dynamic reconstruction requires many deep-MLP queries and several hours of training, limiting efficient processing of long videos and dynamic scenes.
- TRes-NeRF models spatio-temporal fields with an MLP-independent residual building block for fast monocular-video reconstruction.
- Temporal residuals add trainable time-dependent features without increasing network width, enabling smaller MLPs while reducing training time and GPU memory use.
- The rendering pipeline combines rigid transformation, spatial skipping, and efficient Neural Radiance Fields to bypass blank spaces and accelerate rendering.
- The method is validated on challenging tasks for fast, realtime reconstruction of human performers from monocular videos.
2 RELATED WORK
Related work covers NeRF’s reconstruction foundations, its efficiency limitations, neural monocular human reconstruction, temporal-field acceleration, and residual connections.
- NeRF represents scene color and density as functions of spatial points for novel-view synthesis, but rendering requires many 3D samples and MLP evaluations.
- Positional encoding increases effective MLP capacity but adds encoding computation and remains insufficient for efficient training on large-scale complex data.
- Neural representations have become a key technology for reconstructing high-quality 3D human models and generating free-view videos of performers.
- Prior temporal-field methods accelerate NeRF using representations such as sparse 3D grids and spherical harmonics, with some achieving real-time rendering.
- TRes-NeRF models residuals of MLP weights, distinguishing it from residuals added to layer outputs and from residual feature-mesh approaches.
- Efficient representations such as TensoRF, NSVF, and Instant-NGP replace or reduce neural-representation architectures to improve training and rendering speed.
3 METHOD
TRes-NeRF models dynamic human spatiotemporal signals with temporal residual layers, parameter factorization, accelerated sampling, hash-based rendering, and multidimensional losses. These components target efficient monocular-video reconstruction while preserving reconstruction quality and computational performance.
- Residual neural radiance field: TRes-NeRF replaces conventional MLP linear layers with temporal residual layers that add time-dependent trainable parameters to spatiotemporal modeling.The temporal residual enhances MLP capacity without directly increasing neuron count.
- Residual neural radiance field: Temporal residuals are factorized into shared trainable generative parameters, reducing the total number of trainable parameters.The factorization uses temporal coefficients and a shared generative set, with interpolation across temporal sub-intervals.
- Accelerated rendering: An occupancy grid discards samples in empty voxels before radiance-field queries, reducing unnecessary computation around the human body.Rigid transformation and empty-voxel skipping are applied at the input stage.
- Accelerated rendering: Instant-NGP hash tables parameterize multiscale feature grids during rendering to reduce computation while TRes-NeRF captures temporal image-sequence features.The network combines temporal features with spatial location features to evaluate color and density.
- Training loss: The training objective combines color MSE, transparency MSE, and L2 regularization of time-involved fine-detail outputs.The loss framework targets color accuracy, transparency handling, and regularization of model outputs.
4 EXPERIMENT
Experiments on PeopleSnapshot and Neuman compare TRes-NeRF with representative reconstruction methods across quality, speed, challenging scenes, new-pose synthesis, and ablations. TRes-NeRF reports strong reconstruction quality while substantially reducing training and rendering costs.
- Baseline and datasets: Experiments compare TRes-NeRF with Anim-NeRF, Neural Body, and InstantAvatar on reconstruction speed and accuracy.The evaluation uses PeopleSnapshot and Neuman, including challenging human models and dynamic poses.
- Reconstruction quality: TRes-NeRF achieves optimal PSNR and SSIM results compared with InstantAvatar while maintaining similar accuracy to Anim-NeRF and Neural Body.The reported comparison covers four PeopleSnapshot subsets and additional Neuman experiments.
- Computational resources and speed: Nearly 780 times faster operation is reported relative to Anim-NeRF and Neural Body while preserving similar reconstruction accuracy.On an RTX 3090, TRes-NeRF requires one minute, compared with 13 hours for Anim-NeRF and up to 14 hours for Neural Body.
- Qualitative comparison: TRes-NeRF reconstructs fine details and eliminates more artifacts than InstantAvatar in challenging human models.The reported differences are especially noted in detail-rich areas such as faces and legs.
- New pose synthesis: TRes-NeRF generates high-quality reconstructions for complex new poses in PeopleSnapshot experiments.The new-pose evaluation tests challenging pose synthesis rather than only reconstruction of observed poses.
- Ablation study: Temporal residuals improve reconstruction quality and reduce GPU memory consumption, while factor decomposition accelerates training and rendering.The ablations evaluate temporal residuals on Neuman and decomposition of time-related features on PeopleSnapshot.
5 LIMITATIONS
The method quickly reconstructs high-quality human models from monocular video at low hardware cost, but lacks advantages in challenging scenarios without unsupervised constraints.
- The method quickly reconstructs high-quality human models from monocular video at low hardware cost.
- Without unsupervised constraints, the approach may not show advantages in challenging scenarios.
6 CONCLUSION
The paper models complex spatiotemporal signals and accelerates rendering for monocular human-video reconstruction using temporal residuals and multi-resolution hash coding.
- The method introduces temporal residuals into the neural field to model complex spatiotemporal signals for fast monocular-video reconstruction.
- Temporal residuals increase network complexity without adding neurons, while smaller MLPs reduce GPU memory usage and improve reconstruction quality.
- Multi-resolution hash coding maps temporal and spatial features during rendering to improve rendering speed.
- Compared with baseline methods, the approach achieves faster inference and training, lower GPU memory requirements, and higher-quality reconstructions.