Abstract not found

Figure 1: An interactively controllable walking character using parametric motion graphs to smoothly move through an environment. The character is turning around to walk in the user-requested travel direction, depicted by the red arrow on the ground. In this paper, we present an example-based motion synthesis technique that generates continuous streams of high-fidelity, controllable motion for interactive applications, such as video games. Our method uses a new data structure called a parametric motion graph to describe valid ways of generating linear blend transitions between motion clips dynamically generated through parametric synthesis in realtime. Our system specifically uses blending-based parametric synthesis to accurately generate any motion clip from an entire space of motions by blending together examples from that space. The key to our technique is using sampling methods to identify and represent good transitions between these spaces of motion parameterized by a continuously valued parameter. This approach allows parametric motion graphs to be constructed with little user effort. Because parametric motion graphs organize all motions of a particular type, such as reaching to different locations on a shelf, using a single, parameterized graph node, they are highly structured, facilitating fast decision-making for interactive character control. We have successfully created interactive characters that perform sequences of requested actions, such as cartwheeling or punching.

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.

Figure 1: Optimal and sub-optimal solutions for walking a given distance (left) and for picking up an object (right). 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.

Abstract not found

This paper studies the quasi-static motion of large legged robots that have many degrees of freedom. While gaited walking may suffice on easy ground, rough and steep terrain requires unique sequences of footsteps and postural adjustments specifically adapted to the terrain’s local geometric and physical properties. This paper presents a planner that computes these motions by combining graph searching to generate a sequence of candidate footfalls with probabilistic sample-based planning to generate continuous motions that reach these footfalls. To improve motion quality, the probabilistic planner derives its sampling strategy from a small set of motion primitives that have been generated offline. The viability of this approach is demonstrated in simulation for the six-legged lunar vehicle athlete and the humanoid hrp-2 on several example terrains, including one that requires both hand and foot contacts and another that requires rappelling. 1

We present a technique to automatically distill a motion-motif graph from an arbitrary collection of motion capture data. Motion motifs represent clusters of similar motions and together with their encompassing motion graph they lend understandable structure to the contents and connectivity of large motion datasets. They can be used in support of motion compression, the removal of redundant motions, and the creation of blend spaces. This paper develops a string-based motif-finding algorithm which allows for a user-controlled compromise between motif length and the number of motions in a motif. It allows for time warps within motifs and assigns the majority of the input data to relevant motifs. Results are demonstrated for large datasets (more than 100,000 frames) with computation times of tens of minutes.

Figure 1: In order to avoid collisions between the umbrella and the two posts the arm motion was planned in sync with a walking sequence. Editing recorded motions to make them suitable for different sets of environmental constraints is a general and difficult open problem. In this paper we solve a significant part of this problem by modifying full-body motions with an interactive randomized motion planner. Our method is able to synthesize collision-free motions for specified linkages of multiple animated characters in synchrony with the characters ’ full-body motions. The proposed method runs at interactive speed for dynamic environments of realistic complexity. We demonstrate the effectiveness of our interactive motion editing approach with two important applications: (a) motion correction (to remove collisions) and (b) synthesis of realistic object manipulation sequences on top of locomotion.

Figure 1: Intuitive interactive motion generation with deformable motion models: (a) direct manipulation interfaces with point dragging (red point) and fixed handles (green point); (b) pen-based sketching interfaces; (c) motion filtering and foot-skating removal. This paper presents a new motion model deformable motion models for human motion modeling and synthesis. Our key idea is to apply statistical analysis techniques to a set of precaptured human motion data and construct a low-dimensional deformable motion model of the form x = M(α, γ), where the deformable parameters α and γ control the motion’s geometric and timing variations, respectively. To generate a desired animation, we continuously adjust the deformable parameters ’ values to match various forms of userspecified constraints. Mathematically, we formulate the constraintbased motion synthesis problem in a maximum a posteriori (MAP) framework by estimating the most likely deformable parameters from the user’s input. We demonstrate the power and flexibility of our approach by exploring two interactive and easy-to-use interfaces for human motion generation: direct manipulation interfaces and sketching interfaces.

This paper presents a simple and efficient technique for synthesizing high-fidelity motions by attaching, or splicing, the upper-body action of one motion example to the lower-body locomotion of another. Existing splicing algorithms do little more than copy degrees of freedom (DOFs) from one motion onto another. This naïve DOF replacement can produce unrealistic results because it ignores both physical and stylistic correlations between various joints in the body. Our approach uses spatial and temporal relationships found within the example motions to retain the overall posture of the upper-body action while adding secondary motion details appropriate to the timing and configuration of the lower body. By decoupling upper-body action from lower-body locomotion, our motion synthesis technique allows example motions to be captured independently and later combined to create new natural looking motions.