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Xingyi Li

7 accepted papers

2024

DyBluRF: Dynamic Neural Radiance Fields from Blurry Monocular Video

CVPR 2024poster

Recent advancements in dynamic neural radiance field methods have yielded remarkable outcomes. However these approaches rely on the assumption of sharp input images. When faced with motion blur existing dynamic NeRF methods often struggle to generate high-quality novel views. In this paper we propos…

Cited by 10SourcePDFScholar
2024

S-DyRF: Reference-Based Stylized Radiance Fields for Dynamic Scenes

CVPR 2024poster

Current 3D stylization methods often assume static scenes which violates the dynamic nature of our real world. To address this limitation we present S-DyRF a reference-based spatio-temporal stylization method for dynamic neural radiance fields. However stylizing dynamic 3D scenes is inherently chall…

Cited by 4SourcePDFScholar
2024

Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations

CVPR 2024poster

The perception of motion behavior in a dynamic environment holds significant importance for autonomous driving systems wherein class-agnostic motion prediction methods directly predict the motion of the entire point cloud. While most existing methods rely on fully-supervised learning the manual labe…

2024

Semi-supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix

AAAI 2024technical

Class-agnostic motion prediction methods aim to comprehend motion within open-world scenarios, holding significance for autonomous driving systems. However, training a high-performance model in a fully-supervised manner always requires substantial amounts of manually annotated data, which can be bot…