gesticulating_agent_unity VS AlphaPose

Compare gesticulating_agent_unity vs AlphaPose and see what are their differences.


The official code for our paper "A Framework for Integrating Gesture Generation Models into Interactive Conversational Agents", published as a demonstration at AAMAS 2021. (by nagyrajmund)
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gesticulating_agent_unity AlphaPose
1 4
28 6,334
- 1.3%
1.2 8.0
over 1 year ago 13 days ago
Python Python
GNU General Public License v3.0 only GNU General Public License v3.0 or later
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Posts with mentions or reviews of gesticulating_agent_unity. We have used some of these posts to build our list of alternatives and similar projects.


Posts with mentions or reviews of AlphaPose. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-29.

What are some alternatives?

When comparing gesticulating_agent_unity and AlphaPose you can also consider the following projects:

openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

mediapipe - Cross-platform, customizable ML solutions for live and streaming media.

mmpose - OpenMMLab Pose Estimation Toolbox and Benchmark.

lightweight-human-pose-estimation.pytorch - Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.

BlazePose-tensorflow - A third-party Tensorflow Implementation for paper "BlazePose: On-device Real-time Body Pose tracking".

openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.

deep-high-resolution-net.pytorch - The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"

kapao - KAPAO is an efficient single-stage human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.

VIBE - Official implementation of CVPR2020 paper "VIBE: Video Inference for Human Body Pose and Shape Estimation"

yolact_edge - The first competitive instance segmentation approach that runs on small edge devices at real-time speeds.

tfjs - A WebGL accelerated JavaScript library for training and deploying ML models.

UniPose - We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. Additionally, our method is extended to UniPose- LSTM for multi-frame processing and achieves state-of-the- art results for temporal pose estimation in Video. Our re- sults on multiple datase