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arXiv Computer Vision

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

The paper introduces Temporal Residual Neural Radiance Fields for reconstructing dynamic human bodies from monocular video. It builds a temporal residual field independent of MLPs, reduces trainable parameters, speeds up rendering, and employs a multi‑dimensional loss to improve pixel‑level accuracy. Experiments show higher PSNR and SSIM than recent methods while being roughly 780 times faster than Anim‑NeRF and Neural Body.

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arXiv Computer Vision
Aug 27

Forge4D: Feed-Forward 4D Human Reconstruction and Interpolation from Uncalibrated Sparse-view Videos

Forge4D is a feed‑forward model that reconstructs temporally aligned 4D human representations from uncalibrated sparse‑view videos, enabling both novel view and novel time synthesis. It achieves this by jointly streaming 3D Gaussian reconstruction with dense motion prediction, using learnable state tokens for temporal consistency and a self‑supervised retargeting loss for motion prediction. Extensive experiments confirm its effectiveness on in‑domain and out‑of‑domain datasets.

By Yingdong Hu, Yisheng He, Jinnan Chen, Weihao Yuan, Kejie Qiu, Zehong Lin, Siyu Zhu, Zilong Dong, Steven Hoi, Jun Zhang
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arXiv Computer Vision
Oct 1

Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstruction

arXiv:2609.39960v1 Announce Type: new Abstract: 4D scene reconstruction aims to recover the evolving geometry, appearance, and motion of dynamic environments from visual observations. Despite substan...

By Ziren Gong, Guo Chen, Yongjia Li, Yihua Shao, Fabio Tosi, Stefano Mattoccia, Matteo Poggi, Hao Tang, Fei Ma, Shuyan Li, Ziyang Yan, Nicu Sebe, Ling Shao, Jianfei Cai, Qi Tian, Ming-Hsuan Yang
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arXiv AI
Jun 2

A Survey of 3D Reconstruction with Event Cameras

arXiv:2505. 08438v4 Announce Type: replace-cross Abstract: Event cameras are rapidly emerging as powerful vision sensors for 3D reconstruction, uniquely capable of asynchronously capturing per-pixel brightness changes.

By Chuanzhi Xu, Haoxian Zhou, Langyi Chen, Haodong Chen, Zeke Zexi Hu, Zhicheng Lu, Ying Zhou, Vera Chung, Qiang Qu, Weidong Cai
agentsroboticsmultimodal
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arXiv Computer Vision
Oct 16

Benchmarking Efficient & Effective Camera Pose Estimation Strategies for Novel View Synthesis

The paper introduces a benchmark for evaluating Structure-from-Motion (SfM) methods used in novel view synthesis (NVS) such as NeRFs and 3DGS. It compares classical SfM, which relies on local feature matches and bundle adjustment, with recent neural network approaches that regress camera parameters directly for faster but less accurate results. The study finds that reducing feature counts speeds up classical SfM without sacrificing pose accuracy, and that combining neural network initial estimates with classical refinement yields the best balance of efficiency and effectiveness.

By Jhacson Meza, Martin R. Oswald, Torsten Sattler
llmscomputer-visionbenchmarks
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