Humans as a Calibration Pattern: Dynamic 3D Scene Reconstruction from Unsynchronized and Uncalibrated Videos

• URL: http://arxiv.org/abs/2412.19089v1
• Date: Thu, 26 Dec 2024 07:04:20 GMT
• Title: Humans as a Calibration Pattern: Dynamic 3D Scene Reconstruction from Unsynchronized and Uncalibrated Videos
• Authors: Changwoon Choi, Jeongjun Kim, Geonho Cha, Minkwan Kim, Dongyoon Wee, Young Min Kim,
• Abstract summary: Recent setups on dynamic neural field assume input from multi-view videos with known poses. We show that unchronized videos with unknown poses can generate dynamic neural fields if capture stabilizes the video.
• Score: 12.19207713016543
• License:
• Abstract: Recent works on dynamic neural field reconstruction assume input from synchronized multi-view videos with known poses. These input constraints are often unmet in real-world setups, making the approach impractical. We demonstrate that unsynchronized videos with unknown poses can generate dynamic neural fields if the videos capture human motion. Humans are one of the most common dynamic subjects whose poses can be estimated using state-of-the-art methods. While noisy, the estimated human shape and pose parameters provide a decent initialization for the highly non-convex and under-constrained problem of training a consistent dynamic neural representation. Given the sequences of pose and shape of humans, we estimate the time offsets between videos, followed by camera pose estimations by analyzing 3D joint locations. Then, we train dynamic NeRF employing multiresolution rids while simultaneously refining both time offsets and camera poses. The setup still involves optimizing many parameters, therefore, we introduce a robust progressive learning strategy to stabilize the process. Experiments show that our approach achieves accurate spatiotemporal calibration and high-quality scene reconstruction in challenging conditions.

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