Stars
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
The pinocchio auto-rigging / weighting tool - modification
[ECCV 2024] Towards High-Quality 3D Motion Transfer with Realistic Apparel Animation - MMDMC Dataset
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
[CVPR 2023] Code for "VisFusion: Visibility-aware Online 3D Scene Reconstruction from Videos"
Code release for our SIGGRAPH 2022 paper "Diffeomorphic Neural Surface Parameterization for 3D and Reflectance Acquisition"
Official code for CVPR 2023 Paper, HexPlane: A Fast Representation for Dynamic Scenes
This is the code for Deformable Neural Radiance Fields, a.k.a. Nerfies.
Marching cubes implementation for PyTorch environment.
[WACV 2023] Interacting Hand-Object Pose Estimation via Dense Mutual Attention
Official implementation of "Implicit Neural Representations with Periodic Activation Functions"
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
code for cvpr2022 paper "Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction"
Official implementation of the paper: MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (CVPR 2021)
A beautiful, simple, clean, and responsive Jekyll theme for academics
Official implementation of MSR-GCN (ICCV2021 paper)
Code for our CVPR 2020 (ORAL) paper - TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style.
Official implementation for the paper: Generating Smooth Pose Sequences for Diverse Human Motion Prediction
Synthesizing Long-Term 3D Human Motion and Interaction in 3D in CVPR2021
official implementation of CVPR'20 oral paper: Generating 3D People in Scenes without People.: https://ps.is.tuebingen.mpg.de/publications/smpl-x-conditional-vae-prox-scene-constraints
visualization code for 3D human body annotation by EFT (Exemplar Fine-tuning)
A toolkit for developing and comparing reinforcement learning algorithms.