Yuan, Yu-Jie, Kobbelt, Leif, Yang, Jie, Lai, Yu-Kun ![]() Item availability restricted. |
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Abstract
Due to their great performance in representing 3D scene geometry and appearance, Neural Radiance Fields (NeRF) have recently gained a lot of attention in applications like novel view synthesis. Some extensions of NeRFs to dynamic scenes have been proposed, but they either require synchronized multi-view video input or fail for faster motions or longer sequences. In this paper, we propose a novel dynamic NeRF framework, called TPD-NeRF, which takes a single monocular video as input and enables high quality synthesis of novel views for any time point even in highly dynamic scenes. The idea is to first establish local frame-to-frame consistency by training a sub-network that predicts short term offsets and hence generates frame-to-frame correspondences. Applying this network multiple times allows us to propagate correspondences from any frame of the input sequence to one global reference frame. Using the resulting global correspondences as supervision, we can train another sub-network to establish global consistency for the TPD-NeRF. This network effectively maps each dynamic state back to a canonical space, i.e. it captures the global motion in the scene. To further improve the visual quality, we introduce the space-time field network as the canonical NeRF to capture missing dynamic information of the two deformation networks. We extensively evaluate our method and compare it with previous work to demonstrate that our method outperforms existing dynamic NeRF methods.
Item Type: | Conference or Workshop Item (Paper) |
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Date Type: | Publication |
Status: | Published |
Schools: | Schools > Computer Science & Informatics |
ISBN: | 978-981-96-5811-4 |
ISSN: | 0302-9743 |
Date of First Compliant Deposit: | 24 June 2025 |
Date of Acceptance: | 18 December 2024 |
Last Modified: | 04 Jul 2025 15:30 |
URI: | https://orca.cardiff.ac.uk/id/eprint/179291 |
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