Real-time VIBE: Frame by Frame Inference of VIBE (Video Inference for Human Body Pose and Shape Estimation)

Related tags

Deep LearningRT-VIBE
Overview

Real-time VIBE

Inference VIBE frame-by-frame.

Overview

This is a frame-by-frame inference fork of VIBE at [https://github.com/mkocabas/VIBE].

Usage:

import cv2
from vibe.rt.rt_vibe import RtVibe

rt_vibe = RtVibe()
cap = cv2.VideoCapture('sample_video.mp4')
while cap.isOpened():
    ret, frame = cap.read()
    rt_vibe(frame)  # This will open a cv2 window

SMPL Render takes most of the time, which can be closed with vibe_live.render = False

Getting Started

Installation:

# conda must be installed first
wget https://github.com/zc402/RT-VIBE/releases/download/v1.0.0/RT-VIBE.tar.gz
tar zxf RT-VIBE.tar.gz
cd RT-VIBE
# This will create a new conda env called vibe_env
source scripts/install_conda.sh
pip install .  # Install rt-vibe

Run on sample video:

python rt_demo.py  # (This runs sample_video.mp4)
# or
python rt_demo.py --vid_file=multiperson.mp4

Run on camera:

python rt_demo.py --camera

Try with google colab

This notebook provides video and camera inference example.

(there are some dependency errors during pip install, which is safe to ignore. Remember to restart environment after installing pytorch.)

https://colab.research.google.com/drive/1VKXGTfwIYT-ltbbEjhCpEczGpksb8I7o?usp=sharing

Features

  • Make VIBE an installable package
  • Fix GRU hidden states lost between batches in demo.py
  • Add realtime interface which processes the video stream frame-by-frame
  • Decrease GPU memory usage

Explain

  1. Pip installable.

  • This repo renames "lib" to "vibe" ("lib" is not a feasible package name), corrects corresponding imports, adds __init__.py files. It can be installed with:
pip install git+https://github.com/zc402/RT-VIBE

  1. GRU hidden state lost:

  • The original vibe.py reset GRU memory for each batch, which causes discontinuous predictions.

  • The GRU hidden state is reset at:

# .../models/vibe.py
# class TemporalEncoder
# def forward()
y, _ = self.gru(x)

# The "_" is the final hidden state and should be preserved
# https://pytorch.org/docs/stable/generated/torch.nn.GRU.html
  • This repo preserve GRU hidden state within the lifecycle of the model, instead of one batch.
# Fix:

# __init__()
self.gru_final_hidden = None

# forward()
y, self.gru_final_hidden = self.gru(x, self.gru_final_hidden)

  1. Real-time interface

  • This feature makes VIBE run on webcam.

  • Processing steps of the original VIBE :

    • use ffmpeg to split video into images, save to /tmp
    • process the human tracking for whole video, keep results in memory
    • predict smpl params with VIBE for whole video, 1 person at a time.
    • (optional) render and show (frame by frame)
    • save rendered result

  • Processing steps of realtime interface

    • create VIBE model.
    • read a frame with cv2
    • run tracking for 1 frame
    • predict smpl params for each person, keep the hidden states separately.
    • (optional) render and show

  • Changes

    • Multi-person-tracker is modified to receive image instead of image folder.
    • a dataset wrapper is added to convert single image into a pytorch dataset.
    • a rt_demo.py is added to demonstrate the usage.
    • ImageFolder dataset is modified
    • ImgInference dataset is modified
    • requirements are modified to freeze current tracker version. (Class in my repo inherits the tracker and changes its behavior)

  1. Decrease inference memory usage

  • The default batch_size in demo.py needs ~10GB GPU memory
  • Original demo.py needs large vibe_batch_size to keep GRU hidden states
  • Since the GRU hidden state was fixed now, lowering the memory usage won't harm the accuracy anymore.
  • With the default setting in this repo, inference occupies ~1.3GB memory, which makes it runable on low-end GPU.
  • This will slow down the inference a little. The current setting (batchsize==1) reflect actual realtime processing speed.
# Large batch causes OOM in low-end memory card
tracker_batch_size = 12 -> 1
vibe_batch_size = 450 -> 1

Other fixes

Remove seqlen. The seqlen in demo.py has no usage (GRU sequence length is decided in runtime and equals to batch_size). With the fix in this repo, it is safe to set batch_size to 1.

You might also like...
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

Build Type Linux MacOS Windows Build Status OpenPose has represented the first real-time multi-person system to jointly detect human body, hand, facia

null 25.7k Jan 9, 2023
Repository for the paper
Repository for the paper "PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation", CVPR 2021.

PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation Code repository for the paper: PoseAug: A Differentiable Pose Augme

Pyjcsx 328 Dec 17, 2022
Face and Pose detector that emits MQTT events when a face or human body is detected and not detected.
Face and Pose detector that emits MQTT events when a face or human body is detected and not detected.

Face Detect MQTT Face or Pose detector that emits MQTT events when a face or human body is detected and not detected. I built this as an alternative t

Jacob Morris 38 Oct 21, 2022
pytorch implementation of openpose including Hand and Body Pose Estimation.
pytorch implementation of openpose including Hand and Body Pose Estimation.

pytorch-openpose pytorch implementation of openpose including Body and Hand Pose Estimation, and the pytorch model is directly converted from openpose

Hzzone 1.4k Jan 7, 2023
Monocular 3D pose estimation. OpenVINO. CPU inference or iGPU (OpenCL) inference.
Monocular 3D pose estimation. OpenVINO. CPU inference or iGPU (OpenCL) inference.

human-pose-estimation-3d-python-cpp RealSenseD435 (RGB) 480x640 + CPU Corei9 45 FPS (Depth is not used) 1. Run 1-1. RealSenseD435 (RGB) 480x640 + CPU

Katsuya Hyodo 8 Oct 3, 2022
A large-scale video dataset for the training and evaluation of 3D human pose estimation models
A large-scale video dataset for the training and evaluation of 3D human pose estimation models

ASPset-510 ASPset-510 (Australian Sports Pose Dataset) is a large-scale video dataset for the training and evaluation of 3D human pose estimation mode

Aiden Nibali 36 Oct 30, 2022
A large-scale video dataset for the training and evaluation of 3D human pose estimation models
A large-scale video dataset for the training and evaluation of 3D human pose estimation models

ASPset-510 (Australian Sports Pose Dataset) is a large-scale video dataset for the training and evaluation of 3D human pose estimation models. It contains 17 different amateur subjects performing 30 sports-related actions each, for a total of 510 action clips.

Aiden Nibali 25 Jun 20, 2021
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

Expressive Body Capture: 3D Hands, Face, and Body from a Single Image [Project Page] [Paper] [Supp. Mat.] Table of Contents License Description Fittin

Vassilis Choutas 1.3k Jan 7, 2023
Code for
Code for "3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop"

PyMAF This repository contains the code for the following paper: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop Hongwe

Hongwen Zhang 450 Dec 28, 2022
Releases(v1.0.0)
Owner
GitHub Repository https://arxiv.org/abs/1912.05656
The official codes for the ICCV2021 presentation "Uniformity in Heterogeneity: Diving Deep into Count Interval Partition for Crowd Counting"

UEPNet (ICCV2021 Poster Presentation) This repository contains codes for the official implementation in PyTorch of UEPNet as described in Uniformity i

Tencent YouTu Research 15 Dec 14, 2022
An implementation of the Contrast Predictive Coding (CPC) method to train audio features in an unsupervised fashion.

CPC_audio This code implements the Contrast Predictive Coding algorithm on audio data, as described in the paper Unsupervised Pretraining Transfers we

Meta Research 283 Dec 30, 2022
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.13

Keon Lee 140 Dec 21, 2022
Deploy optimized transformer based models on Nvidia Triton server

🤗 Hugging Face Transformer submillisecond inference 🤯 and deployment on Nvidia Triton server Yes, you can perfom inference with transformer based mo

Lefebvre Sarrut Services 1.2k Jan 05, 2023
Perform Linear Classification with Multi-way Data

MultiwayClassification This is an R package to perform linear classification for data with multi-way structure. The distance-weighted discrimination (

Eric F. Lock 2 Dec 15, 2020
Video-Music Transformer

VMT Video-Music Transformer (VMT) is an attention-based multi-modal model, which generates piano music for a given video. Paper https://arxiv.org/abs/

Chin-Tung Lin 5 Jul 13, 2022
PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

SDEdit: Image Synthesis and Editing with Stochastic Differential Equations Project | Paper | Colab PyTorch implementation of SDEdit: Image Synthesis a

536 Jan 05, 2023
Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition

Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition Introduction Run attack: SGADV.py Objective function: foolbox/attacks/gradi

1 Jul 18, 2022
Minimalist Error collection Service compatible with Rollbar clients. Sentry or Rollbar alternative.

Minimalist Error collection Service Features Compatible with any Rollbar client(see https://docs.rollbar.com/docs). Just change the endpoint URL to yo

Haukur Rósinkranz 381 Nov 11, 2022
LegoDNN: a block-grained scaling tool for mobile vision systems

Table of contents 1 Introduction 1.1 Major features 1.2 Architecture 2 Code and Installation 2.1 Code 2.2 Installation 3 Repository of DNNs in vision

41 Dec 24, 2022
Official implementation of "Refiner: Refining Self-attention for Vision Transformers".

RefinerViT This repo is the official implementation of "Refiner: Refining Self-attention for Vision Transformers". The repo is build on top of timm an

101 Dec 29, 2022
auto-tuning momentum SGD optimizer

YellowFin YellowFin is an auto-tuning optimizer based on momentum SGD which requires no manual specification of learning rate and momentum. It measure

Jian Zhang 288 Nov 19, 2022
PRTR: Pose Recognition with Cascade Transformers

PRTR: Pose Recognition with Cascade Transformers Introduction This repository is the official implementation for Pose Recognition with Cascade Transfo

mlpc-ucsd 133 Dec 30, 2022
Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks This is a Python3 / Pytorch implementation of TadGAN paper. The associated

Arun 92 Dec 03, 2022
A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes

A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes

443 Jan 06, 2023
The implemetation of Dynamic Nerual Garments proposed in Siggraph Asia 2021

DynamicNeuralGarments Introduction This repository contains the implemetation of Dynamic Nerual Garments proposed in Siggraph Asia 2021. ./GarmentMoti

42 Dec 27, 2022
Open-Ended Commonsense Reasoning (NAACL 2021)

Open-Ended Commonsense Reasoning Quick links: [Paper] | [Video] | [Slides] | [Documentation] This is the repository of the paper, Differentiable Open-

(Bill) Yuchen Lin 31 Oct 19, 2022
Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

Status: Archive (code is provided as-is, no updates expected) InfoGAN Code for reproducing key results in the paper InfoGAN: Interpretable Representat

OpenAI 1k Dec 19, 2022
Pytorch implementation of the paper "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization"

Pytorch implementation of the paper "Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization"

Dongkyu Lee 4 Sep 18, 2022
Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device" @ CAD&Graphics2019

PortraitNet Code for the paper "PortraitNet: Real-time portrait segmentation network for mobile device". @ CAD&Graphics 2019 Introduction We propose a

265 Dec 01, 2022