We present a framework to synthesize character movements based on high level parameters, such that the produced movements respect the manifold of human motion, trained on a large motion capture dataset. The learned motion manifold, which is represented by the hidden units of a convolutional autoencoder, represents motion data in sparse components which can be combined to produce a wide range of complex movements. To map from high level parameters to the motion manifold, we stack a deep feedforward neural network on top of the trained autoencoder. This network is trained to produce realistic motion sequences from parameters such as a curve over the terrain that the character should follow, or a target location for punching and kicking. The feedforward control network and the motion manifold are trained independently, allowing the user to easily switch between feedforward networks according to the desired interface, without re-training the motion manifold. Once motion is generated it can be edited by performing optimization in the space of the motion manifold. This allows for imposing kinematic constraints, or transforming the style of the motion, while ensuring the edited motion remains natural. As a result, the system can produce smooth, high quality motion sequences without any manual pre-processing of the training data.

/pdf/a-deep-learning-framework-for-character-motion-synthesis-and-1bxrj5s5d7.pdf

A deep learning framework for character motion synthesis and editing

This paper introduces a new generative statistical model that allows for human motion analysis and synthesis at both semantic and kinematic levels. Our key idea is to decouple complex variations of human movements into finite structural variations and continuous style variations and encode them with a concatenation of morphable functional models. This allows us to model not only a rich repertoire of behaviors but also an infinite number of style variations within the same action. Our models are appealing for motion analysis and synthesis because they are highly structured, contact aware, and semantic embedding. We have constructed a compact generative motion model from a huge and heterogeneous motion database (about two hours mocap data and more than 15 different actions). We have demonstrated the power and effectiveness of our models by exploring a wide variety of applications, ranging from automatic motion segmentation, recognition, and annotation, and online/offline motion synthesis at both kinematics and behavior levels to semantic motion editing. We show the superiority of our model by comparing it with alternative methods.

Motion graphs++: a compact generative model for semantic motion analysis and synthesis

Many compelling applications would become feasible if novice users had the ability to synthesize high quality human motion based only on a simple sketch and a few easily specified constraints. We approach this problem by representing the desired motion as an interpolation of two time-scaled paths through a motion graph. The graph is constructed to support interpolation and pruned for efficient search. We use an anytime version of A* search to find a globally optimal solution in this graph that satisfies the user's specification. Our approach retains the natural transitions of motion graphs and the ability to synthesize physically realistic variations provided by interpolation. We demonstrate the power of this approach by synthesizing optimal or near optimal motions that include a variety of behaviors in a single motion.

/pdf/construction-and-optimal-search-of-interpolated-motion-3ncfyqnbpq.pdf

Construction and optimal search of interpolated motion graphs

We present a new approach to realtime character animation with interactive control. Given a corpus of motion capture data and a desired task, we automatically compute near-optimal controllers using a low-dimensional basis representation. We show that these controllers produce motion that fluidly responds to several dimensions of user control and environmental constraints in realtime. Our results indicate that very few basis functions are required to create high-fidelity character controllers which permit complex user navigation and obstacle-avoidance tasks.

/pdf/near-optimal-character-animation-with-continuous-control-87dwb87qlp.pdf

Near-optimal character animation with continuous control

Interactive, task-guided character controllers must be agile and responsive to user input, while retaining the flexibility to be readily authored and modified by the designer. Central to a method's ease of use is its capacity to synthesize character motion for novel situations without requiring excessive data or programming effort. In this work, we present a technique that animates characters performing user-specified tasks by using a probabilistic motion model, which is trained on a small number of artist-provided animation clips. The method uses a low-dimensional space learned from the example motions to continuously control the character's pose to accomplish the desired task. By controlling the character through a reduced space, our method can discover new transitions, tractably precompute a control policy, and avoid low quality poses.

/pdf/continuous-character-control-with-low-dimensional-embeddings-3y2hka0ote.pdf

Continuous character control with low-dimensional embeddings

This paper proposes a methodology that allows users to control character's motion interactively but continuously. Inspired by the work of Gleicher et al. [GSKJ03], we propose a semi-automatic method to build fat graphs where a node corresponds to a pose and its incoming and outgoing edges represent the motion segments starting from and ending at similar poses. A group of edges is built into a fat edge that parameterizes similar motion segments into a blendable form. Employing the existing motion transition and blending methods, our run-time system allows users to control a character interactively in continuous parameter spaces with conventional input devices such as joysticks and the mice. The capability of the proposed methodology is demonstrated through several applications. Although our method has some limitations on motion repertories and qualities, it can be adapted to a number of real-world applications including video games and virtual reality applications.

/pdf/fat-graphs-constructing-an-interactive-character-with-n4mspst9od.pdf

Fat graphs: constructing an interactive character with continuous controls

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Fat graphs: constructing an interactive character with continuous controls