In this paper, we propose a novel approach to improve a given surface mapping through local refinement. The approach receives an established mapping between two surfaces and follows four phases: (i) inspection of the mapping and creation of a sparse set of landmarks in mismatching regions; (ii) segmentation with a low-distortion region-growing process based on flattening the segmented parts; (iii) optimization of the deformation of segmented parts to align the landmarks in the planar parameterization domain; and (iv) aggregation of the mappings from segments to update the surface mapping. In addition, we propose a new approach to deform the mesh in order to meet constraints (in our case, the landmark alignment of phase (iii)). We incrementally adjust the cotangent weights for the constraints and apply the deformation in a fashion that guarantees that the deformed mesh will be free of flipped faces and will have low conformal distortion. Our new deformation approach, Iterative Least Squares Conformal Mapping (ILSCM), outperforms other low-distortion deformation methods. The approach is general, and we tested it by improving the mappings from different existing surface mapping methods. We also tested its effectiveness by editing the mappings for a variety of 3D objects.

Abstract We present a general method for transferring skeletons and skinning weights between characters with distinct mesh topologies. Our pipeline takes as inputs a source character rig (consisting of a mesh, a transformation hierarchy of joints, and skinning weights) and a target character mesh. From these inputs, we compute joint locations and orientations that embed the source skeleton in the target mesh, as well as skinning weights to bind the target geometry to the new skeleton. Our method consists of two key steps. We first compute the geometric correspondence between source and target meshes using a semi‐automatic method relying on a set of markers. The resulting geometric correspondence is then used to formulate attribute transfer as an energy minimization and filtering problem. We demonstrate our approach on a variety of source and target bipedal characters, varying in mesh topology and morphology. Several examples demonstrate that the target characters behave well when animated with either forward or inverse kinematics. Via these examples, we show that our method preserves subtle artistic variations; spatial relationships between geometry and joints, as well as skinning weight details, are accurately maintained. Our proposed pipeline opens up many exciting possibilities to quickly animate novel characters by reusing existing production assets.

Abstract Synthesizing controllers for rotational movements in feature space is an open research problem and is particularly challenging because of the need to precisely regulate the character's global orientation, angular momentum and inertia. This paper presents feature‐based controllers for a wide variety of rotational movements, including cartwheels, dives and flips. We show that the controllers can be made robust to large external disturbances by using a time‐invariant control scheme. The generality of the control laws is demonstrated by providing examples of the flip controller with different apexes, the diving controller with different heights and styles, the cartwheel controller with different speeds and straddle widths, etc. The controllers do not rely on any input motion or offline optimization.

We propose a fully automatic method for specifying influence weights for closed-form skinning methods, such as linear blend or dual quaternion skinning. Our method is designed to work with production meshes that may contain non-manifold geometry, be non-watertight, have intersecting triangles, or be comprised of multiple connected components. Starting from a character rest pose mesh and skeleton hierarchy, we first voxelize the input geometry. The resulting sparse voxelization is then used to calculate binding weights, based on the geodesic distance between each voxel lying on a skeleton "bone" and all non-exterior voxels. This yields smooth weights at interactive rates, without time-constants, iteration parameters, or costly optimization at bind or pose time. By decoupling weight assignment from distance computation we make it possible to modify weights interactively, at pose time, without additional pre-processing or computation. This allows artists to assess impact of weight selection in the context in which they are used.

This paper presents the first method for full-body trajectory optimization of physics-based human motion that does not rely on motion capture, specified key-poses, or periodic motion. Optimization is performed using a small set of simple goals, for example, one hand should be on the ground, or the center-of-mass should be above a particular height. These objectives are applied to short spacetime windows which can be composed to express goals over an entire animation. Specific contact locations needed to achieve objectives are not required by our method. We show that the method can synthesize many different kinds of movement, including walking, hand walking, breakdancing, flips, and crawling. Most of these movements have never been previously synthesized by physics-based methods.

This paper introduces an approach to control of physics-based characters based on high-level features of movement, such as center-of-mass, angular momentum, and end-effectors. Objective terms are used to control each feature, and are combined by a prioritization algorithm. We show how locomotion can be expressed in terms of a small number of features that control balance and end-effectors. This approach is used to build controllers for human balancing, standing jump, and walking. These controllers provide numerous benefits: human-like qualities such as arm-swing, heel-off, and hip-shoulder counter-rotation emerge automatically during walking; controllers are robust to changes in body parameters; control parameters and goals may be modified at run-time; control parameters apply to intuitive properties such as center-of-mass height; and controllers may be mapped onto entirely new bipeds with different topology and mass distribution, without modifications to the controller itself. No motion capture or off-line optimization process is used.

This paper presents a physics-based locomotion controller based on online planning. At each time-step, a planner optimizes locomotion over multiple phases of gait. Stance dynamics are modeled using a simplified Spring-Load Inverted (SLIP) model, while flight dynamics are modeled using projectile motion equations. Full-body control at each instant is optimized to match the instantaneous plan values, while also maintaining balance. Different types of gaits, including walking, running, and jumping, emerge automatically, as do transitions between different gaits. The controllers can traverse challenging terrain and withstand large external disturbances, while following high-level user commands at interactive rates.

We introduce an optimization framework called prioritized optimization control, in which a nested sequence of objectives are optimized so as not to conflict with higher-priority objectives. We focus on the case of quadratic objectives and derive an efficient recursive solver for this case. We show how task-space control can be formulated in this framework, and demonstrate the technique on three sample control problems. The proposed formulation supports acceleration, torque, and bilateral force constraints, while simplifying reasoning about task-space control. This scheme unifies prioritized task-space and optimization-based control. Our method computes control torques for all presented examples in real-time.

This paper presents a new dynamic walking controllers for quadrupedal robots with compliant legs. The algorithm implements a "walking bound" gait, requires only one actuator per leg at the hip, and commands a constant hip velocity during stance. The algorithm has been implemented successfully on our Scout II quadruped robot and has yielded stable walking for ranges of operating conditions with minimal reliance on feedback. The experimental data is used to illustrate important considerations on torque generation that need to be taken into account both for successful implementations and realistic modeling of legged robots. In addition, we quantify the energetics of our walking experiments via the specific resistance and document dramatic differences between mechanical and electrical power.