Animating characters can be a time-consuming process in a production pipeline, but there are various techniques that can be used to expedite the process. One such technique is key frame interpolation, which involves animating only the main poses, while the computer draws or interpolates the missing key frames based on the ones the artist drew.
Motion capture is another technique that has gained popularity, particularly in video game production. It involves recording human body movements using motion capture tools and using that data to drive animation. However, blending between different movements still requires an artist’s touch, particularly in film animation.
While motion capture is useful for animating characters in video games, blending different movements still requires artistic input. This is even more critical for film animation. However, in video games, players may transition between movements rapidly and in real-time, making it impractical to rely on an animator to make those transitions on the fly. unless if every time a video gamer buys your game you ship them an animator to make transitions while there playing.
To solve this problem, various tools and techniques have been developed to transition between different motion capture poses, but often the resulting animation appears unnatural. This is where deep learning character control comes in handy.
this project has become a comprehensive framework for data-driven character animation, including data processing, network training and runtime control, developed in Unity3D / Tensorflow / PyTorch. This repository demonstrates using neural networks for animating biped locomotion, quadruped locomotion, and character-scene interactions with objects and the environment, plus sports and fighting games. Further advances on this research will continue being added to this project.
this Ai controler uses a novel neural network architecture called the Periodic Autoencoder that can learn periodic features from large unstructured motion datasets in an unsupervised manner. The character movements are decomposed into multiple latent channels that capture the non-linear periodicity of different body segments while progressing forward in time.
it uses deep learning framework to produce a large variety of martial arts movements in a controllable manner from raw motion capture data imitating animation layering using neural networks with the aim to overcome typical challenges when mixing, blending and editing movements from unaligned motion sources. The system can be used for offline and online motion generation alike, provides an intuitive interface to integrate with animator workflows, and is relevant for real-time applications such as computer games.
though this is not a commercial tool you can download yet, its possible that some studios a already implementing something similar in there production pipeline, tools like these most of the time replace a lot of jobs, for example this Ai has the potential to replace hundreds of artists as it can do the job of several artists and get the job done in a short time frame.
Ai tools like this also create more opportunities for small studios and individual artist, because Ai tools like this can do work that would require alot of money a big crew and alot of time, a small team or an individual artist can leverage this tool to produce work that’s on per with tripple A studios
speaking of tools that you can leverage to improve your production value, have you seen hdri maker , what hdri maker makes is more than hdri it a complete scene that you can fully customize to your liking with parallax occlusion, ground that can capture shadows and more give it a try you wont regret it