Abstract We present a fully automated technique that segments raster images into smooth shaded regions and reconstructs them using an optimal mix of solid fills, linear gradients, and radial gradients. Our method leverages a novel discontinuity‐aware segmentation strategy and gradient reconstruction algorithm to accurately capture intricate shading details and produce compact Bézier curve representations. Extensive evaluations on both designer‐created art and generative images demonstrate that our approach achieves high visual fidelity with minimal geometric complexity and fast processing times. This work offers a robust and versatile solution for converting detailed raster images into scalable vector graphics, addressing the evolving needs of modern design workflows.

Example-based shape deformation allows a mesh to be easily manipulated or animated with simple inputs. As the user pulls parts of the shape, the rest of the mesh automatically changes in an intuitive way by drawing from a set of exemplars. This provides a way for virtual shapes or characters to be easily authored and manipulated, or for a set of drawings to be animated with simple inputs. We describe a new approach for example-based inverse kinematic mesh manipulation which generates high quality deformations for a wide range of inputs, and in particular works well even when provided stylized or "cartoony" examples. This approach is fast enough to run in real time, reliably uses the artist's input shapes in an intuitive way even for highly nonphysical deformations, and provides added expressiveness by allowing the input shapes to be utilized in a way which spatially varies smoothly across the resulting deformed mesh. This allows for rich and detailed deformations to be created from a small set of input shapes, and gives an easy way for a set of sketches to be brought alive with simple click-and-drag inputs.

We present a technique for analyzing a set of animal gaits to predict the gait of a new animal from its shape alone. This method works on a wide range of bipeds and quadrupeds, and adapts the motion style to the size and shape of the animal. We achieve this by combining inverse optimization with sparse data interpolation. Starting with a set of reference walking gaits extracted from sagittal plane video footage, we first use inverse optimization to learn physically motivated parameters describing the style of each of these gaits. Given a new animal, we estimate the parameters describing its gait with sparse data interpolation, then solve a forward optimization problem to synthesize the final gait. To improve the realism of the results, we introduce a novel algorithm called joint inverse optimization which learns coherent patterns in motion style from a database of example animal-gait pairs. We quantify the predictive performance of our model by comparing its synthesized gaits to ground truth walking motions for a range of different animals. We also apply our method to the prediction of gaits for dinosaurs and other extinct creatures.

Abstract There exists a large body of research devoted to creating real time interactive locomotion controllers which are targeted at some specific class of character, most often humanoid bipeds. Relatively little work, however, has been done on approaches which are applicable to creatures with a wide range of different forms – partially due to the lack of easily obtainable motion‐capture data for animals and fictional creatures. We show how a locomotion controller can be created despite this dearth of data by synthesizing it from scratch. Our method only requires as input a description of the shape of the animal, the gaits which it can perform, and a model of the task or tasks for which the controller will be used. From this a sequence of motion clips are automatically synthesized and assembled into a motion graph which defines a physically realistic controller capable of performing the specified tasks. The method attempts to minimize the computational time required to generate this controller and we show its effectiveness at creating interactive controllers for a range of tasks for bipeds, tripeds, and quadrupeds.

We propose a novel representation of motion data and control that enables characters with both highly agile responses to user input and natural handling of arbitrary external disturbances. The representation organizes motion data as samples in a high dimensional generalization of a vector field we call a 'motion field'. Our runtime motion synthesis mechanism freely 'flows' in the motion field and is capable of creating novel and natural motions that are highly-responsive to the real time user input, and generally not explicitly specified in the data.

The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games. We present a fully automatic method for generating intelligent real-time controllers for characters in such a game. Our approach uses game theory to deal with the ramifications of the characters acting simultaneously, and generates controllers which employ both long-term planning and an intelligent use of randomness. Our results exhibit nuanced strategies based on unpredictability, such as feints and misdirection moves, which take into account and exploit the possible strategies of an adversary. The controllers are generated by examining the interaction between the rules of the game and the motions generated from a parametric motion graph. This involves solving a large-scale planning problem, so we also describe a new technique for scaling this process to higher dimensions.

We present a fully automatic method for generating gaits and morphologies for legged animal locomotion. Given a specific animal's shape we can determine an efficient gait with which it can move. Similarly, we can also adapt the animal's morphology to be optimal for a specific locomotion task. We show that determining such gaits is possible without the need to specify a good initial motion, and without manually restricting the allowed gaits of each animal. Our approach is based on a hybrid optimization method which combines an efficient derivative-aware spacetime constraints optimization with a derivative-free approach able to find non-local solutions in high-dimensional discontinuous spaces. We demonstrate the effectiveness of this approach by synthesizing dynamic locomotions of bipeds, a quadruped, and an imaginary five-legged creature.

We present a conceptually simple approach to generalizing force-directed methods for graph layout from Euclidean geometry to Riemannian geometries. Unlike previous work on non-Euclidean force-directed methods, ours is not limited to special classes of graphs, but can be applied to arbitrary graphs. The method relies on extending the Euclidean notions of distance, angle, and force-interactions to smooth non-Euclidean geometries via projections to and from appropriately chosen tangent spaces. In particular, we formally describe the calculations needed to extend such algorithms to hyperbolic and spherical geometries. We also study the theoretical and practical considerations that arise when working with non-Euclidean geometries.