2023 ICCV ICCV 2023

3D Motion Magnification: Visualizing Subtle Motions from Time-Varying Radiance Fields

Abstract

Motion magnification helps us visualize subtle, imperceptible motion. However, prior methods only work for 2D videos captured with a fixed camera. We present a 3D motion magnification method that can magnify subtle motions from scenes captured by a moving camera, while supporting novel view rendering. We represent the scene with time-varying radiance fields and leverage the Eulerian principle for motion magnification to extract and amplify the variation of the embedding of a fixed point over time. We study and validate our proposed principle for 3D motion magnification using both implicit and tri-plane-based radiance fields as our underlying 3D scene representation. We evaluate the effectiveness of our method on both synthetic and real-world scenes captured under various camera setups.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning
🧭 Keyword Pioneer — 3d motion magnification
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Machine Learning, Mathematics & Optimization, Robotics