Translating motions from a real user onto a virtual embodied avatar is a key challenge for character animation in the metaverse. In this work, we present a novel generative framework that enables mapping from a set of sparse sensor signals to a full body avatar motion in real-time while faithfully preserving the motion context of the user. In contrast to existing techniques that require training a motion prior and its mapping from control to motion separately, our framework is able to learn the motion manifold as well as how to sample from it at the same time in an end-to-end manner. To achieve that, we introduce a technique called codebook matching which matches the probability distribution between two categorical codebooks for the inputs and outputs for synthesizing the character motions. We demonstrate this technique can successfully handle ambiguity in motion generation and produce high quality character controllers from unstructured motion capture data. Our method is especially useful for interactive applications like virtual reality or video games where high accuracy and responsiveness are needed.

This paper introduces a novel data-driven motion in-betweening system to reach target poses of characters by making use of phases variables learned by a Periodic Autoencoder. The approach utilizes a mixture-of-experts neural network model, in which the phases cluster movements in both space and time with different expert weights. Each generated set of weights then produces a sequence of poses in an autoregressive manner between the current and target state of the character. In addition, to satisfy poses which are manually modified by the animators or where certain end effectors serve as constraints to be reached by the animation, a learned bi-directional control scheme is implemented to satisfy such constraints. Using phases for motion in-betweening tasks sharpen the interpolated movements, and furthermore stabilizes the learning process. Moreover, more challenging movements beyond locomotion behaviors can be synthesized. Additionally, style control is enabled between given target keyframes. The framework can compete with state-of-the-art methods for motion in-betweening in terms of motion quality and generalization, especially in the existence of long transition durations. This framework contributes to faster prototyping workflows for creating animated character sequences, which is of enormous interest for the game and film industry.

This work shows the idea from the paper "Neural Animation Layering for Synthesizing Martial Arts Movements" in an easy-to-use augmentation tool that enables users without animation background to quickly generate novel motion combinations.

How does it work?
Given a set of body joint trajectories, we can synthesize a very accurate pose with a simple network architecture. In order to reconstruct the motion from these control signals, we first learn a function that maps this complete past and future series to the root transformation of our character, and after that run another function to produce the pose.

We can setup to perform a large variety of character motions via different control inputs: the control trajectories can be sampled piece-wise from reference animation clips or motion matching, and the motion generator enables layering movements together. In order to apply layering techniques, we can either add some offsets to the trajectories to create motion variations, or mix trajectories from reference motions to create novel motion combinations. For instance, we can combine the upper-body trajectories of reference clip A together with lower-body trajectories of clip B: The tool enables us to select the reference motions from which we want to combine different body parts. We can assign the clip for each body part by mapping their unique colors in the user interface. While showing a preview of the reference motions, we can quickly prototype new combinations and the tool feeds the different control trajectories to the motion generator. Using techniques like these can achieve cost- and time savings for animation generation and capturing workflows.

Creating animation can be a time consuming, repetitive and difficult process. Therefore, advanced deep learning models for motion synthesis such as the Mode-Adaptive Neural Network (MANN) are able to generate realistic movements that follow high-level control signals in a plausible manner. In this work, we can generate high quality animation sequences just with a few clicks using the authoring system that learns from a large set of motion capture data and enables to generate such control signals through an intuitive user interface. To do so, the user can specify a path, desired actions at a defined time or position, and the system does not playback, but generates realistic movements that follow those control signals in a data-driven manner. Additionally, a new dataset is augmented by enhancing synthetic motions that are manipulated by inverse kinematics and then trained jointly with motion capture data. Overall, the system provides in Unity an offline control for different quadruped character movements, such as locomotion and different stylizations e.g. jumping, sitting, sneaking, eating, and hydrating.

In Rapid Fire you can shoot your way through space with your friends in an arcade-lego themed designed environment. You can choose either to play alone or in a team of 3 players who aim to survive the attacks of the 4th player for a period of time. In case you are alone in front of the screen right now - dont worry! You can select an AI to control your teams or/and enemy spaceships. You will have to dodge obstacles, learn on how to use your abilities and defeat your enemies. Become more powerful by collecting power-ups and try to survive! Enjoy and remember: you will either win or lose as a team!

The minigame was part of a main game builded in the course "Interactive Game Development 2021" at the University of Hamburg and was developed using Unity3D (C#) and Blender over the course of 10 weeks. Here, you can find the trailer and GitHub repository of the main game.

In Autochess players place characters on a grid-shaped battlefield during a preparation phase, who then fight the opposing team's characters without any further direct input from the player. The project is in an early phase and still under development.

In this project, an indoor navigation system was created for the Volksparkstadion of the HSV in Hamburg. It was investigated how viewers can be navigated through the stadium by using the shortest route to their seats. As a basis for this, a 3D model based on architectural plans of the stadium was created using computer vision. For this, the plans of the different levels were transformed into graphs and the contrast was increased to sharpen the structures to highlight seats, entrances, exits, and stairs. Then, doors and texts were detected using machine learning models to lay a grid over the walkable area that allows navigation with nodes and edges. The graph is constructed to be easily adaptable, to allow support for different events for which the navigation has to change as part of the stadium is not accessible for example in the case of concerts. For the application, the 3D model was connected to an interface that was developed with python and HTML. The navigation system can be used by entering the seat location and Dijkstra's algorithm calculates the shortest path which is visualized in the interface. The starting point is one of the stadium entrances, which needs to be replaced by the location of the viewer in real-world facilitation. In perspective, an extension of the application can also consider stopovers at food or merchandise counters and take care of an even distribution of visitor flows with the help of the GPS function of mobile phones to reduce waiting times and crowded corridors.

In this project, a platform for various applications such as a social network and a messenger has been deployed. It is still under development, but can be used for testing purposes. All data is stored in Mongo DB. Python and Javascript is used to handle all processes. The social platform provides all standard functionality such as posting, liking, following, sharing, etc.

In this project I worked as a dataset creator for The Game Walktrough Corpus (GWTC). The dataset contains 12.295 unique walkthrough documents that cover a total of 6.117 games, which results in two solutions per game on average. Using Scrapy crawlers a pipeline was build to collect English and German language walkthroughs from multiple platforms. The text content was extracted and after pre-processing converted into a generic uniform hierarchical TEI/XML markup. Additionally, various of metadata are included in the walkthrough documents, which were gathered from RAWG and Steam.

Please refer to the development page and published paper above for a more detailed project information, dataset description, observations, and how to potentially use this corpus for your own research projects.