Hugo Bertiche

Orcid: 0000-0002-6632-1902

According to our database1, Hugo Bertiche authored at least 16 papers between 2017 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Image2Garment: Simulation-ready Garment Generation from a Single Image.
CoRR, January, 2026

2023
Towards Efficient and Realistic Animation of 3D Garments with Deep Learning.
PhD thesis, 2023

Blowing in the Wind: CycleNet for Human Cinemagraphs from Still Images.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Neural Cloth Simulation.
ACM Trans. Graph., 2022

2021
PBNS: physically based neural simulation for unsupervised garment pose space deformation.
ACM Trans. Graph., 2021

Deep Unsupervised 3D Human Body Reconstruction from a Sparse set of Landmarks.
Int. J. Comput. Vis., 2021

Neural Implicit Surfaces for Efficient and Accurate Collisions in Physically Based Simulations.
CoRR, 2021

DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Deep Parametric Surfaces for 3D Outfit Reconstruction from Single View Image.
Proceedings of the 16th IEEE International Conference on Automatic Face and Gesture Recognition, 2021

2020
SMPLR: Deep learning based SMPL reverse for 3D human pose and shape recovery.
Pattern Recognit., 2020

Physically Based Neural Simulator for Garment Animation.
CoRR, 2020

DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation.
CoRR, 2020

Learning Cloth Dynamics: 3D+Texture Garment Reconstruction Benchmark.
Proceedings of the NeurIPS 2020 Competition and Demonstration Track, 2020

CLOTH3D: Clothed 3D Humans.
Proceedings of the Computer Vision - ECCV 2020, 2020

2018
SMPLR: Deep SMPL reverse for 3D human pose and shape recovery.
CoRR, 2018

2017
Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-Temporal Handcrafted Features and Deep Strategies.
Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017


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