Summary of Motion-oriented Compositional Neural Radiance Fields For Monocular Dynamic Human Modeling, by Jaehyeok Kim et al.
Motion-Oriented Compositional Neural Radiance Fields for Monocular Dynamic Human Modeling
by Jaehyeok Kim, Dongyoon Wee, Dan Xu
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces MoCo-NeRF, a framework for free-viewpoint rendering of monocular human videos. The approach uses novel non-rigid motion modeling to capture complex cloth dynamics in dynamic clothed humans. Unlike conventional methods that model non-rigid motions as spatial deviations, MoCo-NeRF models them as radiance residual fields, utilizing rigid radiance fields as a prior to reduce learning complexity. The framework utilizes multiresolution hash encoding (MHE) to learn canonical T-pose representations and radiance residual fields concurrently. Additionally, it supports simultaneous training of multiple subjects through global MHE and learnable identity codes. The paper presents state-of-the-art performance on ZJU-MoCap and MonoCap datasets in both single- and multi-subject settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoCo-NeRF is a new way to create realistic videos of people from different angles. Right now, it’s hard to make these kinds of videos because our bodies move in complex ways when we’re wearing clothes that can get tangled or blow around in the wind. The old way of making these videos was too complicated and slow, so researchers came up with a new approach called MoCo-NeRF. It uses something called radiance fields to capture the movements of clothed humans more accurately. This new method is much faster and easier to use than the old one, and it can even be used to make videos of multiple people at the same time. |
