mmpose

Pros of Detectron2

• Broader scope, covering object detection, segmentation, and more
• Extensive documentation and tutorials
• Highly modular and customizable architecture

Cons of Detectron2

• Steeper learning curve for beginners
• Less focused on pose estimation specifically
• Requires more setup and configuration for pose-related tasks

Code Comparison

MMPose example:

from mmpose.apis import inference_top_down_pose_model, init_pose_model

pose_model = init_pose_model(pose_config, pose_checkpoint)
pose_results, _ = inference_top_down_pose_model(pose_model, image)

Detectron2 example:

from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor

cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml"))
predictor = DefaultPredictor(cfg)
outputs = predictor(image)

MMPose is more specialized for pose estimation tasks, offering a simpler API for common use cases. Detectron2 provides a more general-purpose framework, requiring additional setup but offering greater flexibility for various computer vision tasks.

Pros of OpenPose

• Real-time performance on CPU and GPU
• Supports multi-person pose estimation
• Includes hand, face, and foot keypoint detection

Cons of OpenPose

• Less flexible architecture compared to MMPose
• Limited support for recent pose estimation models
• Fewer pre-trained models available

Code Comparison

OpenPose:

#include <openpose/pose/poseExtractor.hpp>

auto poseExtractor = op::PoseExtractorCaffe::getInstance(poseModel, netInputSize, outputSize, keypointScaleMode, num_gpu_start);
poseExtractor->forwardPass(netInputArray, imageSize, scaleInputToNetInputs);

MMPose:

from mmpose.apis import init_pose_model, inference_top_down_pose_model

pose_model = init_pose_model(config, checkpoint)
pose_results, _ = inference_top_down_pose_model(pose_model, image, person_results)

OpenPose offers real-time performance and multi-person pose estimation out of the box, making it suitable for applications requiring immediate results. However, MMPose provides a more flexible architecture, supports a wider range of recent pose estimation models, and offers more pre-trained models. MMPose's Python-based implementation also makes it easier to integrate into existing machine learning pipelines.

Pros of tfjs-models

• Runs in web browsers and Node.js environments
• Offers a wide range of pre-trained models beyond pose estimation
• Easier integration with JavaScript and web applications

Cons of tfjs-models

• Limited to TensorFlow.js ecosystem
• May have lower performance compared to native implementations
• Fewer pose estimation models and less flexibility in model customization

Code Comparison

mmpose:

from mmpose.apis import inference_top_down_pose_model, init_pose_model

pose_model = init_pose_model(config, checkpoint)
pose_results, _ = inference_top_down_pose_model(pose_model, image)

tfjs-models:

import * as poseDetection from '@tensorflow-models/pose-detection';

const detector = await poseDetection.createDetector(poseDetection.SupportedModels.MoveNet);
const poses = await detector.estimatePoses(image);

Key Differences

• mmpose offers more comprehensive pose estimation capabilities and research-oriented features
• tfjs-models provides easier integration with web technologies and broader model selection
• mmpose is Python-based, while tfjs-models is JavaScript-based
• mmpose has more advanced customization options for pose estimation models
• tfjs-models is more suitable for client-side applications and rapid prototyping

Pros of google-research

• Broader scope, covering various research areas beyond pose estimation
• Regularly updated with cutting-edge research from Google's teams
• Extensive documentation and explanations for each project

Cons of google-research

• Less focused on pose estimation specifically
• May be more challenging to navigate due to its diverse content
• Some projects might be less production-ready compared to mmpose

Code comparison

mmpose:

from mmpose.apis import inference_top_down_pose_model, init_pose_model

pose_model = init_pose_model(pose_config, pose_checkpoint)
pose_results, _ = inference_top_down_pose_model(pose_model, image)

google-research (PoseNet example):

import tensorflow as tf
import posenet

model = posenet.load_model(101)
output = model(tf.convert_to_tensor(image))
keypoints = output['heatmapScores']

Summary

While google-research offers a wide range of research projects and frequent updates, mmpose provides a more focused and potentially more production-ready solution for pose estimation tasks. The choice between the two depends on whether you need a specialized pose estimation tool or want to explore various research areas.

Pros of human-pose-estimation.pytorch

• Simpler and more lightweight implementation
• Easier to understand and modify for beginners
• Focuses specifically on the SimpleBaseline method

Cons of human-pose-estimation.pytorch

• Limited to a single pose estimation method
• Less actively maintained and updated
• Fewer pre-trained models and datasets supported

Code Comparison

mmpose:

from mmpose.apis import inference_top_down_pose_model, init_pose_model

pose_model = init_pose_model(config, checkpoint)
pose_results, _ = inference_top_down_pose_model(pose_model, image)

human-pose-estimation.pytorch:

from pose_estimation import get_pose_net
from pose_utils import get_final_preds

model = get_pose_net()
heatmaps = model(image)
preds = get_final_preds(heatmaps)

mmpose offers a more comprehensive and modular approach, while human-pose-estimation.pytorch provides a simpler, more direct implementation. mmpose's API is designed for flexibility across multiple models and datasets, whereas human-pose-estimation.pytorch focuses on a single method with a straightforward interface.