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PeterZhouSZ's Projects

igr icon igr

Implicit Geometric Regularization for Learning Shapes

igsolver icon igsolver

IGsolver is a C++ library implements several basic optimization algorithms

ihmr icon ihmr

Official repo for our 3DV 2021 paper "Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements".

iicnet icon iicnet

[ICCV2021] IICNet: A Generic Framework for Reversible Image Conversion

ijcai2019-drl4ser icon ijcai2019-drl4ser

The python implementation for paper "Towards Discriminative Representation Learning for Speech Emotion Recognition" in IJCAI-2019

ikc icon ikc

Implementation of 'Blind Super-Resolution With Iterative Kernel Correction' (CVPR2019)

il_video_inpainting icon il_video_inpainting

Official pytorch implementation for "An Internal Learning Approach to Video Inpainting" (ICCV 2019)

ilvr_adm icon ilvr_adm

ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models (ICCV 2021 Oral)

im-net icon im-net

The improved code for paper "Learning Implicit Fields for Generative Shape Modeling".

im2flow icon im2flow

Im2Flow: Motion Hallucination from Static Images for Action Recognition (CVPR 2018)

im2smpl icon im2smpl

Predicts a SMPL model given a RGB image using clues from HMR, Simplify, AlphaPose, etc.

im2vec icon im2vec

Im2Vec Synthesizing Vector Graphics without Vector Supervision CVPR 2021 Oral

image-adaptive-3dlut icon image-adaptive-3dlut

Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time

image-align icon image-align

Variants of the classic Lucas-Kanade image alignment algorithm

image-blur-map icon image-blur-map

A reproduction of the results of 'Estimating Spatially Varying Defocus Blur From A Single Image' by Zhu et. al.

image-contrast-enhancement icon image-contrast-enhancement

Python implementation of "A New Image Contrast Enhancement Algorithm Using Exposure Fusion Framework", CAIP2017

image-filtering-and-hybrid-images icon image-filtering-and-hybrid-images

This repository presents is an image convolution function (image filtering) used it to create hybrid images. The technique was invented by Oliva, Torralba, and Schyns in 2006, and published in a paper at SIGGRAPH. High frequency image content tends to dominate perception but, at a distance, only low frequency (smooth) content is perceived. By blending high and low frequency content, we can create a hybrid image that is perceived differently at different distances. This project is in a computer vision course.

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