# Sync-NeRF: Generalizing Dynamic NeRFs to Unsynchronized Videos

arXiv: https://arxiv.org/abs/2310.13356 · HTML: https://mlsanity.com/paper/2310.13356/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 15 of 15)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance](https://mlsanity.com/paper/2301.10241/) (2023)
- [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022)
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [HexPlane: A Fast Representation for Dynamic Scenes](https://mlsanity.com/paper/2301.09632/) (2023)
- [Fast Dynamic Radiance Fields with Time-Aware Neural Voxels](https://mlsanity.com/paper/2205.15285/) (2022)
- [Self-Calibrating Neural Radiance Fields](https://mlsanity.com/paper/2108.13826/) (2021)
- [Monocular Dynamic View Synthesis: A Reality Check](https://mlsanity.com/paper/2210.13445/) (2022)
- [Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time](https://mlsanity.com/paper/2202.08614/) (2022)
- [Mixed Neural Voxels for Fast Multi-view Video Synthesis](https://mlsanity.com/paper/2212.00190/) (2022)
- [Streaming Radiance Fields for 3D Video Synthesis](https://mlsanity.com/paper/2210.14831/) (2022)
- [HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputs](https://mlsanity.com/paper/2112.02789/) (2021)

## Built on by (most cited first, 16 of 16)

- [Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis](https://mlsanity.com/paper/2312.16812/) (2023)
- [Per-Gaussian Embedding-Based Deformation for Deformable 3D Gaussian Splatting](https://mlsanity.com/paper/2404.03613/) (2024)
- [Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation](https://mlsanity.com/paper/2505.13215/) (2025)
- [Dynamic Scene Reconstruction: Recent Advance in Real-time Rendering and Streaming](https://mlsanity.com/paper/2503.08166/) (2025)
- [DyTact: Capturing Dynamic Contacts in Hand-Object Manipulation](https://mlsanity.com/paper/2506.03103/) (2025)
- [Humans as a Calibration Pattern: Dynamic 3D Scene Reconstruction from Unsynchronized and Uncalibrated Videos](https://mlsanity.com/paper/2412.19089/) (2024)
- [Compensating Spatiotemporally Inconsistent Observations for Online Dynamic 3D Gaussian Splatting](https://mlsanity.com/paper/2505.01235/) (2025)
- [PackUV: Packed Gaussian UV Maps for 4D Volumetric Video](https://mlsanity.com/paper/2602.23040/) (2026)
- [Visual Sync: Multi-Camera Synchronization via Cross-View Object Motion](https://mlsanity.com/paper/2512.02017/) (2025)
- [SharpTimeGS: Sharp and Stable Dynamic Gaussian Splatting via Lifespan Modulation](https://mlsanity.com/paper/2602.02989/) (2026)
- [Time-Archival Camera Virtualization for Sports and Visual Performances](https://mlsanity.com/paper/2602.15181/) (2026)
- [Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstruction](https://mlsanity.com/paper/2609.39960/) (2026)
- [Enhanced Velocity Field Modeling for Gaussian Video Reconstruction](https://mlsanity.com/paper/2507.23704/) (2025)
- [SyncTrack4D: Cross-Video Motion Alignment and Video Synchronization for Multi-Video 4D Gaussian Splatting](https://mlsanity.com/paper/2512.04315/) (2025)
- [ASTRA: Asynchronous Spatio-Temporal Reconstruction via Trajectory Alignment](https://mlsanity.com/paper/2608.02006/) (2026)
- [UniFusion: Sparse-View 4D Reconstruction via Unified Spatio-temporal Depth Alignment](https://mlsanity.com/paper/2609.05888/) (2026)

## Abstract (arXiv)

Recent advancements in 4D scene reconstruction using neural radiance fields (NeRF) have demonstrated the ability to represent dynamic scenes from multi-view videos. However, they fail to reconstruct the dynamic scenes and struggle to fit even the training views in unsynchronized settings. It happens because they employ a single latent embedding for a frame while the multi-view images at the same frame were actually captured at different moments. To address this limitation, we introduce time offsets for individual unsynchronized videos and jointly optimize the offsets with NeRF. By design, our method is applicable for various baselines and improves them with large margins. Furthermore, finding the offsets naturally works as synchronizing the videos without manual effort. Experiments are conducted on the common Plenoptic Video Dataset and a newly built Unsynchronized Dynamic Blender Dataset to verify the performance of our method. Project page: https://seoha-kim.github.io/sync-nerf