You can do that from your in-game settings. Download the Pose Player mod and extract the two files inside the. Place both files into your Sims 4 Mods folder as shown below. You can also put custom poses into your Mods folder if you would like them to be played in the game. Start the game up and click on your Sim as you usually would when you want to tell them to do something. You will notice a new choice appears in the menu of actions. Select Pose By Name.
Note: the sim must be standing or they will not pose with the animation you choose. You need to enter a clip name into the box that pops up. There is a list of all the EA clips posted in the same download as the Pose Player itself.
You can highlight a clip name on the list, copy it, and paste it into the pop up box in the game. This is what the box will look like. Once you've entered a clip pose name click the check button. If you want you can click on any other Sim, even Sims that aren't the active Sim, and tell them to pose too.
They can perform a different pose than the active Sim is performing or they can perform the same one. When you want the Sim to stop performing a pose click them again and select Stop Posing from the menu. If you want your Sim to perform a custom default override pose you need to type in the name of whichever EA pose the custom pose overwrites.
MoveNet outperforms PoseNet on a variety of datasets, especially in images with fitness action images. Therefore, we recommend using MoveNet over PoseNet. Performance benchmark numbers are generated with the tool described here. Accuracy mAP numbers are measured on a subset of the COCO dataset in which we filter and crop each image to contain only one person. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.
For details, see the Google Developers Site Policies. Install Learn Introduction. TensorFlow Lite for mobile and embedded devices. TensorFlow Extended for end-to-end ML components. TensorFlow v2. Pre-trained models and datasets built by Google and the community.
Ecosystem of tools to help you use TensorFlow. Part 1: Pose estimation — the basics What is pose estimation? Categories of pose estimation Why does pose estimation matter? Part 2: How does pose estimation work? The goal of our machine learning models are to track these keypoints in images and videos.
Above: Tracking keypoints on a person playing ping pong. Categories of pose estimation When working with people, these keypoints represent major joints like elbows, knees, wrists, etc. Why does pose estimation matter? Back to top. Primary techniques for pose estimation In general, deep learning architectures suitable for pose estimation are based on variations of convolutional neural networks CNNs.
Basic structure Deep learning models for pose estimation come in a few varieties related to the top-down and bottom-up approaches discussed above. Stacked-Hourglass networks, Mask-RCNN, and other encoder-decoder networks Pure encoder-decoder networks take an image as input and output heatmaps for each keypoint. Convolutional Pose Machines Convolutional pose machines build on the encoder-decoder architecture by iteratively refining heatmap predictions using additional network layers and feature extraction.
How pose estimation works on the edge If your use case requires that pose estimation work in real-time, without internet connectivity, or on private data, you might be considering running your pose estimation model directly on an edge device like a mobile phone or IoT board. Here are a few tips and tricks to ensure your models are ready for edge deployment: Use MobileNet-based architectures for your encoder. This architecture makes use of layer types like depthwise separable convolutions that require fewer parameters and less computation while still providing solid accuracy.
Add a width multiplier to your model so you can adjust the number of parameters in your network to meet your computation and memory constraints.
The number of filters in a convolution layer, for example, greatly impacts the overall size of your model. Many papers and open-source implementations will treat this number as a fixed constant, but most of these models were never intended for mobile or edge use. Adding a parameter that multiplies the base number of filters by a constant fraction allows you to modulate the model architecture to fit the constraints of your device.
For some tasks, you can create much, much smaller networks that perform just as well as large ones. Shrink models with quantization , but beware of accuracy drops. Quantizing model weights can save a bunch of space, often reducing the size of a model by a factor of 4 or more. However, accuracy will suffer. Make sure you test quantized models rigorously to determine if they meet your needs.
Input and output sizes can be smaller than you think! Using human pose estimation to track human movement could also power a number of other experiences, including but not limited to: AI-powered sports coaches Workplace activity monitoring Crowd counting and tracking e. Robotics Traditionally, industrial robotics have employed 2D vision systems to enable robots to perform their various tasks.
Part 4: Resources We hope the above overview was helpful in understanding the basics of pose estimation and how it can be used in the real world.
All rights reserved. A Guide to OpenPose in Build real-world AI vision. Gaudenz Boesch. September 20, Need Computer Vision? Pose Estimation with OpenPose A human pose skeleton denotes the orientation of an individual in a particular format. Keypoints detected by OpenPose on the Coco Dataset. Who created OpenPose? What are the features of OpenPose? How to use OpenPose? How Does OpenPose Work? How OpenPose Works — Source Overview of the Pipeline a entire image as input b two-branch CNN to jointly predict confidence maps for body part detection c estimate part affinity fields for parts association d set of bipartite matchings to associate body parts candidates e assemble them into full-body poses for all people in the image OpenPose vs.
Alpha-Pose vs. Mask R-CNN OpenPose is one of the most well-renowned bottom-up approaches for real-time multi-person body pose estimation. OpenPose vs. Mask R-CNN Architecture The Bottom Line Real-time multi-person pose estimation is an important element in enabling machines to visually comprehend and analyze humans and their interactions. Related Articles.
Show more. What Is Deep Learning? All-in-one platform to build computer vision applications without code. Show me more. Feature Index. Computer Vision. Visual Programming. Cloud Workspace. Analytics Dashboard. Device Management. Smart City.
0コメント