Yunlu Chen — accepted AI-conference papers

2024

Doubly Hierarchical Geometric Representations for Strand-based Human Hairstyle Generation

Yunlu Chen, Francisco Vicente Carrasco, Christian Häne, Giljoo Nam, Jean-Charles Bazin, Fernando De la Torre

NeurIPS 2024poster

We introduce a doubly hierarchical generative representation for strand-based 3D hairstyle geometry that progresses from coarse, low-pass filtered guide hair to densely populated hair strands rich in high-frequency details. We employ the Discrete Cosine Transform (DCT) to separate low-frequency stru…

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2024

Graph Neural Networks for Learning Equivariant Representations of Neural Networks

Miltiadis Kofinas, Boris Knyazev, Yan Zhang, Yunlu Chen, Gertjan J. Burghouts, Efstratios Gavves +2

ICLR 2024oral

Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors. However, existing approaches either overlook the inherent permutation…

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2024

Ray-Distance Volume Rendering for Neural Scene Reconstruction

Ruihong Yin*, Yunlu Chen, Sezer Karaoglu, Theo Gevers

ECCV 2024poster

"Existing methods in neural scene reconstruction utilize the Signed Distance Function (SDF) to model the density function. However, in indoor scenes, the density computed from the SDF for a sampled point may not consistently reflect its real importance in volume rendering, often due to the influence…

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2022

3D Equivariant Graph Implicit Functions

Yunlu Chen, Basura Fernando, Hakan Bilen, Matthias Nießner, Efstratios Gavves

ECCV 2022poster

"In recent years, neural implicit representations have made remarkable progress in modeling of 3D shapes with arbitrary topology. In this work, we address two key limitations of such representations, in failing to capture local 3D geometric fine details, and to learn from and generalize to shapes wi…

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2021

Neural Feature Matching in Implicit 3D Representations

Yunlu Chen, Basura Fernando, Hakan Bilen, Thomas Mensink, Efstratios Gavves

ICML 2021spotlight

Recently, neural implicit functions have achieved impressive results for encoding 3D shapes. Conditioning on low-dimensional latent codes generalises a single implicit function to learn shared representation space for a variety of shapes, with the advantage of smooth interpolation. While the benefit…

Cited by 6 Source ↗PDF ↗code ↗Scholar ↗

2020

PointMixup: Augmentation for Point Clouds

Yunlu Chen, Vincent Tao Hu, Efstratios Gavves, Thomas Mensink, Pascal Mettes, Pengwan Yang +1

ECCV 2020poster

This paper introduces data augmentation for point clouds by interpolation between examples. Data augmentation by interpolation has shown to be a simple and effective approach in the image domain. Such a mixup is however not directly transferable to point clouds, as we do not have a one-to-one corres…

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