These motorized joints can be used to drive the ragdoll to be in different poses, or to even animate the ragdoll: Just as you can move your own arm by activating your muscles, a ragdoll can move its rigid bodies by driving the joints like motors of a robot. A joint does this by constraining the relative motion between any two rigid bodies.įor the human ragdoll this is analogous to a simplified muscle in a real human. These rigid bodies are affected by forces due to gravity and contacts with the ground, so we need some way for them to stay together. A human ragdoll might have fewer moving parts than a fluid simulation, but the constraints and contacts in its motions can make it difficult to model.Įach of the major body parts of a character is represented by what is called a rigid body. We were interested in whether these ragdoll motions could be modelled by a neural network and what the applications for such a neural network were.īy collecting hundreds of thousands of different transitions of these systems, a neural network can be trained to faithfully recreate the movement of fluids, the flow of clothing on a character, or the motion of rigid objects. In the last few years, machine learning researches have successfully applied neural networks to the task of modeling complex dynamical systems. We found that world models are not only faster than previous approaches for training character controllers, but can easily scale to a large numbers of motions in a single controller. Inspired by this idea, we present a world model-based method for learning physical, interactive character controllers. However, virtual characters do not typically have this world model, which could explain why animating them in a virtual, physically simulated environment has historically been so difficult. It’s the reason why bouncing on a trampoline is sensible but bouncing on a sidewalk seems silly – it doesn’t conform to our world model. This model shows us how events might unfold before those events have happened, and its predictions are based on what we’ve encountered before. In machine learning, this is referred to as an agent’s world model. These are all illustrations of how our mind creates a model of the world we interact with. Professional athletes use ‘mental practise’ to envision themselves executing the sequence of steps needed to perform an advanced maneuver. Or picture our fingers plucking a tricky guitar riff. We can imagine in our mind the path our arm would take as it sweeps across a desk to pick up a cup of coffee. Todd also criticized the game for containing flattering and off-putting imagery, (such as the 3D image of the player's body used as a menu (compared to an airport scanner), and showing the feed from the camera on-screen during a routine) and concluded that "there isn't much in Your Shape that will get you off the couch.Humans have a wonderful ability to play out scenarios in our head before they happen. While its camera was noted as a positive aspect, Your Shape was panned for taking too much of a " no frills" and female-oriented approach in comparison to other games in its category (such as Wii Fit and EA Sports Active) including few customization options and no minigames, bland routines consisting of only straight aerobics with little variation. GameSpot's Brett Todd gave Your Shape a 5.0 out of 10. However, reviewers also noted that a large play area was required to make the camera function with the game properly. Its motion tracking camera was met with positive reception for its ease of setup and accuracy allowing Your Shape to not require any special accessories or controllers for use. Your Shape was released to mixed reviews by critics. release of the game would feature an endorsement and appearances by model and comedian Jenny McCarthy, appearing as its in-game " workout buddy" to follow along with and motivate players while performing the exercise routines. The development of the camera also led to the development of a fitness video game to go with the camera, which spawned Your Shape. Ubisoft's design team in Barcelona began developing the camera in 2007 after they had seen a prototype of motion tracking technology developed by PrimeSense, a company who would later provide the motion sensing functionality for Microsoft's "Project Natal" (later released as Kinect) two years later in 2009. Your Shape was developed as a companion to the Ubisoft Motion Tracking Camera.