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junhaozhang98's Projects

awesome-anything icon awesome-anything

General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX

awesome-video-diffusion icon awesome-video-diffusion

A curated list of recent diffusion models for video generation, editing, restoration, understanding, etc.

cylinder3d icon cylinder3d

Rank 1st in the leaderboard of SemanticKITTI semantic segmentation (Nov. 2020)

gda-net icon gda-net

Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud

graphcmr icon graphcmr

Repository for the paper "Convolutional Mesh Regression for Single-Image Human Shape Reconstruction"

hmd icon hmd

Detailed Human Shape Estimation from a Single Image by Hierarchical Mesh Deformation (CVPR2019 Oral)

hrformer icon hrformer

This is an official implementation of our NeurIPS 2021 paper "HRFormer: High-Resolution Transformer for Dense Prediction".

mmdetection3d icon mmdetection3d

OpenMMLab's next-generation platform for general 3D object detection.

open-muse icon open-muse

Open reproduction of MUSE for fast text2image generation.

rerender_a_video icon rerender_a_video

[SIGGRAPH Asia 2023] Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation

show-1 icon show-1

Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

slowfast icon slowfast

PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

swin-transformer icon swin-transformer

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

swin-transformer-object-detection icon swin-transformer-object-detection

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

tune-a-video icon tune-a-video

Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation

vit-pytorch icon vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

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