12/16/2023 0 Comments Dog motion capture![]() However, there is still a long way before computer vision methods could capture natural motion of arbitary animals in industrial-grade. ![]() Inspired by remarkable techniques for markerless human motion capture, a few excellent literatures appear for animal modeling and reconstruction such as SMAL and DeepLabCut. ![]() Recently, markerless animal motion capture and 3D reconstruction attracts more and more attention in computer vision community. I recommend CV4Animal workshop in CVPR 2021 ( ) because it is a good collection of recent advances in animal pose estimation area! It has collected various kinds of datasets. I recommend MMPose for 2D animal pose estimation. To make it easy to track, I sort the papers using the timepoint I find them (not publication time), and add label badges to show paper features. The 2nd CV4Animals Workshop in CVPR 2022 ( ) presents many excellent works on rodent 3D reconstruction! Here is the Ph.D thesis of SEMIH GÜNEL ( ), who is the author of well-known DeepFl圓D and LiftPose3D. The program of CV4Animals Workshop in CVPR 2023 is available! ![]() It also learned a few that weren’t dog-like at all, like little dances from animations created by an artist.My own paper "MAMMAL" is published online in Nature Communications now! They ended up being remarkably dog-like behaviors, despite the robot’s lack of a dog anatomy the researchers even got it to chase its nonexistent tail, spinning around in circles. But some of those behaviors result in a better gait than others. The system has already made its most catastrophic mistakes in the computer simulation-remember those demerits-so the robot doesn’t have to make them in the real world. “The idea is that if we train the simulation with enough diversity, it might learn a good enough set of strategies, such that one of those strategies will work in the real world,” Peng says.Īll of these strategies are reasonable for the robot to pull off, by the way-they don’t want it to move so rapidly or violently that it will injure itself or humans. They randomized friction in the simulation, for instance, and tweaked the latency between when you send the robot a command and when it actually executes the order. So Peng and his colleagues built not one definitive robot simulation, but a range of possibilities for what the robot’s behavior could be. The new system could be the first steps (sorry) toward robots that learn to move not thanks to exhaustive coding, but by watching videos of animals running and jumping. Not only that, its learned gait is faster than the fastest gait provided by the manufacturer of the robot-though in fairness it’s not yet as stable. But thanks to this translation work, Laikago has learned to move like a real-life canine. (The robot is named, by the way, after Laika, the Soviet space dog who was the first animal to orbit Earth.)Ī robot works quite differently than a biological dog it has motors instead of muscles, and in general it’s a lot stiffer. Then they port those algorithms into the physical version of Laikago. The researchers then translate the digital version of the real dog into a digital version of their four-legged robot-Laikago, which has a rectangular body and skinny legs. ![]() They gather motion-capture videos from a public dataset, then feed that data into a simulator to create a digital version of the pooch. Over at Google, researchers have a secret weapon to teach robots to move that’s both less taxing and more adorable: dogs. Pity the engineers who have to write out all that code. What comes so easily to you-OK maybe not backflips, just walking-requires extreme coordination, which roboticists have to replicate, a kind of dance of motors working in concert. But what you don’t see is the wildly complex underlying code that makes it possible. What you see when Boston Dynamics’ humanoid robot does a backflip or its Spot dog robot fights off a human and opens a door is incredible hardware engineering, to be sure. ![]()
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