Temporal Residual Neural Radiance Fields for Monocular Video Dynamic Human Body Reconstruction

2026-09-04

AI Summary

本文提出使用时序残差神经辐射场方法，以提高单目视频动态人体重建的质量和效率，通过构建时序残差场、减少可训练参数和设计多维损失函数来优化重建效果。

Abstract

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.

Problem

• Temporal Residual Neural Radiance Fields
• Dynamic Scene Reconstruction
• Efficiency
• Human 3D Modeling

Innovation

• Temporal Residual Neural Radiance Fields
• Dynamic Scene Human 3D Modeling
• Efficient Reconstruction
• Multi-dimensional Loss Function
• Time Efficiency