Sync-NeRF: Generalizing Dynamic NeRFs to Unsynchronized Videos

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 always 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

Authors

• Seoha Kim Yonsei University
• Jeongmin Bae Yonsei University
• Youngsik Yun Yonsei University
• Hahyun Lee Electronics and Telecommunications Research Institute
• Gun Bang Electronics and Telecommunications Research Institute
• Youngjung Uh Yonsei University

Keywords

• CV: 3D Computer Vision
• CV: Computational Photography, Image & Video Synthesis
• General