We introduce Garment3DGen a new method to synthesize 3D garment assets from a base mesh given a single input image as guidance. Our proposed approach allows users to generate 3D textured clothes based on both real and synthetic images, such as those generated by text prompts. The generated assets can be directly draped and simulated on human bodies. We leverage the recent progress of image-to-3D diffusion methods to generate 3D garment geometries. However, since these geometries cannot be utilized directly for downstream tasks, we propose to use them as pseudo ground-truth and set up a mesh deformation optimization procedure that deforms a base template mesh to match the generated 3D target. Carefully designed losses allow the base mesh to freely deform towards the desired target, yet preserve mesh quality and topology such that they can be simulated. Finally, we generate high-fidelity texture maps that are globally and locally consistent and faithfully capture the input guidance, allowing us to render the generated 3D assets. With Garment3DGen users can generate the simulation-ready 3D garment of their choice without the need of artist intervention. We present a plethora of quantitative and qualitative

Designing real and virtual garments is becoming extremely demanding with rapidly changing fashion trends and increasing need for synthesizing realisticly dressed digital humans for various applications. This necessitates creating simple and effective workflows to facilitate authoring sewing patterns customized to garment and target body shapes to achieve desired looks. Traditional workflow involves a trial-and-error procedure wherein a mannequin is draped to judge the resultant folds and the sewing pattern iteratively adjusted until the desired look is achieved. This requires time and experience. Instead, we present a data-driven approach wherein the user directly indicates desired fold patterns simply by sketching while our system estimates corresponding garment and body shape parameters at interactive rates. The recovered parameters can then be further edited and the updated draped garment previewed. Technically, we achieve this via a novel shared shape space that allows the user to seamlessly specify desired characteristics across multimodal input without requiring to run garment simulation at design time. We evaluate our approach qualitatively via a user study and quantitatively against test datasets, and demonstrate how our system can generate a rich quality of on-body garments targeted for a range of body shapes while achieving desired fold characteristics. Code and data are available at our project webpage.

When bringing animated characters to life, artists often augment the primary motion of a figure by adding secondary animation -- subtle movement of parts like hair, foliage or cloth that complements and emphasizes the primary motion. Traditionally, artists add secondary motion to animated illustrations only through arduous manual effort, and often eschew it entirely. Emerging ``live' performance applications allow both novices and experts to perform the primary motion of a character, but only a virtuoso performer could manage the degrees of freedom needed to specify both primary and secondary motion together. This paper introduces physically-inspired rigs that propagate the primary motion of layered, illustrated characters to produce plausible secondary motion. These composable elements are rigged and controlled via a small number of parameters to produce an expressive range of effects. Our approach supports a variety of the most common secondary effects, which we demonstrate with an assortment of characters of varying complexity.

Live animation of 2D characters is a new form of storytelling that has started to appear on streaming platforms and broadcast TV. Unlike traditional animation, human performers control characters in real time so that they can respond and improvise to live events. Current live animation systems provide a range of animation controls, such as camera input to drive head movements, audio for lip sync, and keyboard shortcuts to trigger discrete pose changes via artwork swaps. However, managing all of these controls during a live performance is challenging. In this work, we present a new interactive system that specifically addresses the problem of triggering artwork swaps in live settings. Our key contributions are the design of a multi-touch triggering interface that overlays visual triggers around a live preview of the character, and a predictive triggering model that leverages practice performances to suggest pose transitions during live performances. We evaluate our system with quantitative experiments, a user study with novice participants, and interviews with professional animators.

We present a mesh-based, interpolatory method for interactively creating artist-directed inbetweens from arbitrary sets of 2D drawing shapes without rigging. To enable artistic freedom of expression we remove prior restrictions on the range of possible changes between shapes; we support interpolation with extreme deformation and unrestricted topology change. To do this, we extend discrete variational interpolation by introducing a consistent multimesh structure over drawings, a Comesh Optimization algorithm that optimizes our multimesh for both intra- and inter-mesh quality, and a new shape-space energy that efficiently supports arbitrary changes and can prevent artwork overlap when desired. Our multimesh encodes specified correspondences that guide interpolation paths between shapes. With these correspondences, an efficient local-global minimization of our energy interpolates n-way between drawing shapes to create inbetweens. Our Comesh Optimization enables artifact-free minimization by building consistent meshes across drawings that improve both the quality of per-mesh energy discretization and inter-mesh mapping distortions, while guaranteeing a single, compatible triangulation. We implement our method in a test-bed interpolation system that allows interactive creation and editing of animations from sparse key drawings with arbitrary topology and shape change.

In recent years, there is a growing need for communicating complex data in an accessible graphical form. Existing visualization creation tools support automatic visual encoding, but lack flexibility for creating custom design; on the other hand, freeform illustration tools require manual visual encoding, making the design process time-consuming and error-prone. In this paper, we present Data-Driven Guides (DDG), a technique for designing expressive information graphics in a graphic design environment. Instead of being confined by predefined templates or marks, designers can generate guides from data and use the guides to draw, place and measure custom shapes. We provide guides to encode data using three fundamental visual encoding channels: length, area, and position. Users can combine more than one guide to construct complex visual structures and map these structures to data. When underlying data is changed, we use a deformation technique to transform custom shapes using the guides as the backbone of the shapes. Our evaluation shows that data-driven guides allow users to create expressive and more accurate custom data-driven graphics.

Animation artists enjoy the benefits of simulation but do not want to be held back by its constraints. Artist-directed dynamics seeks to resolve this need with a unified method that combines simulation with classical keyframing techniques. The combination of these approaches improves upon both extremes: simulation becomes more customizable and keyframing becomes more automatic. Examining our system in the context of the twelve fundamental animation principles reveals that it stands out for its treatment of exaggeration and appeal. Our system accommodates abrupt jumps, large plastic deformations, and makes it easy to reuse carefully crafted animations.

Abstract Traditional methods for creating dynamic objects and characters from static drawings involve careful tweaking of animation curves and/or simulation parameters. Sprite sheets offer a more drawing‐centric solution, but they do not encode timing information or the logic that determines how objects should transition between poses and cannot generalize outside the given drawings. We present an approach for creating dynamic sprites that leverages sprite sheets while addressing these limitations. In our system, artists create a drawing, deform it to specify a small number of example poses, and indicate which poses can be interpolated. To make the object move, we design a procedural simulation to navigate the pose manifold in response to external or user‐controlled forces. Powerful artistic control is achieved by allowing the artist to specify both the pose manifold and how it is navigated, while physics is leveraged to provide timing and generality. We used our method to create sprites with a range of different dynamic properties. Copyright © 2014 John Wiley & Sons, Ltd.

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.

Traditional methods for creating dynamic objects and characters from static drawings involve careful tweaking of animation curves and/or simulation parameters. Sprite sheets offer a more drawing-centric solution, but they do not encode timing information or the logic that determines how objects should transition between poses and cannot generalize outside the given drawings. We present an approach for creating dynamic sprites that leverages sprite sheets while addressing these limitations. In our system, artists create a drawing, deform it to specify a small number of example poses, and indicate which poses can be interpolated. To make the object move, we design a procedural simulation to navigate the pose manifold in response to external or user-controlled forces. Powerful artistic control is achieved by allowing the artist to specify both the pose manifold and how it is navigated, while physics is leveraged to provide timing and generality. We used our method to create sprites with a range of different dynamic properties.

Abstract Physical simulation and other procedural methods are increasingly popular tools in interactive applications because they generate complex and reactive behaviors given only a few parameter settings. This automation accelerates initial implementation, but also introduces a need to tune the available parameters until the desired behaviors emerge. These adjustments are typically performed iteratively, with the designer repeatedly running—and interacting with—the procedural animation with different parameter settings. Such a process is inaccurate, time consuming, and requires deep understanding and intuition, as parameters often have complex, nonlinear effects. Instead, we propose that designers construct physics storyboards to accelerate the process of tuning interactive, procedural animations. Physics storyboards are collections of space‐time snapshots that highlight critical events and outcomes. They can be used to summarize the effects of parameter changes (without requiring the designer to perform extensive play‐testing); and—when augmented with designer‐provided evaluation functions—allow automatic parameter selection. We describe our implementation of this method, including how we use sampling to ensure that our automatically‐selected parameters generalize, and how we time‐warp user input to adapt it to changing parameters. We validate our implementation by using it to perform various design tasks in three example games.

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.

Skinning transformations are a popular way to articulate shapes and characters. However, traditional animation interfaces require all of the skinning transformations to be specified explicitly, typically using a control structure (a rig). We propose a system where the user specifies only a subset of the degrees of freedom and the rest are automatically inferred using nonlinear, rigidity energies. By utilizing a low-order model and reformulating our energy functions accordingly, our algorithm runs orders of magnitude faster than previous methods without compromising quality. In addition to the immediate boosts in performance for existing modeling and real time animation tools, our approach also opens the door to new modes of control: disconnected skeletons combined with shape-aware inverse kinematics. With automatically generated skinning weights, our method can also be used for fast variational shape modeling.

We present a novel shape completion technique for creating temporally coherent watertight surfaces from real-time captured dynamic performances. Because of occlusions and low surface albedo, scanned mesh sequences typically exhibit large holes that persist over extended periods of time. Most conventional dynamic shape reconstruction techniques rely on template models or assume slow deformations in the input data. Our framework sidesteps these requirements and directly initializes shape completion with topology derived from the visual hull. To seal the holes with patches that are consistent with the subject's motion, we first minimize surface bending energies in each frame to ensure smooth transitions across hole boundaries. Temporally coherent dynamics of surface patches are obtained by unwarping all frames within a time window using accurate interframe correspondences. Aggregated surface samples are then filtered with a temporal visibility kernel that maximizes the use of nonoccluded surfaces. A key benefit of our shape completion strategy is that it does not rely on long-range correspondences or a template model. Consequently, our method does not suffer error accumulation typically introduced by noise, large deformations, and drastic topological changes. We illustrate the effectiveness of our method on several high-resolution scans of human performances captured with a state-of-the-art multiview 3D acquisition system.

Computer Aided Design (CAD) typically involves tasks such as adjusting the camera perspective and assembling pieces in free space that require specifying 6 degrees of freedom (DOF). The standard approach is to factor these DOFs into 2D subspaces that are mapped to the x and y axes of a mouse. This metaphor is inherently modal because one needs to switch between subspaces, and disconnects the input space from the modeling space. In this paper, we propose a bimanual hand tracking system that provides physically-motivated 6-DOF control for 3D assembly. First, we discuss a set of principles that guide the design of our precise, easy-to-use, and comfortable-to-use system. Based on these guidelines, we describe a 3D input metaphor that supports constraint specification classically used in CAD software, is based on only a few simple gestures, lets users rest their elbows on their desk, and works alongside the keyboard and mouse. Our approach uses two consumer-grade webcams to observe the user's hands. We solve the pose estimation problem with efficient queries of a precomputed database that relates hand silhouettes to their 3D configuration. We demonstrate efficient 3D mechanical assembly of several CAD models using our hand-tracking system.

Object deformation with linear blending dominates practical use as the fastest approach for transforming raster images, vector graphics, geometric models and animated characters. Unfortunately, linear blending schemes for skeletons or cages are not always easy to use because they may require manual weight painting or modeling closed polyhedral envelopes around objects. Our goal is to make the design and control of deformations simpler by allowing the user to work freely with the most convenient combination of handle types. We develop linear blending weights that produce smooth and intuitive deformations for points, bones and cages of arbitrary topology. Our weights, called bounded biharmonic weights, minimize the Laplacian energy subject to bound constraints. Doing so spreads the influences of the controls in a shape-aware and localized manner, even for objects with complex and concave boundaries. The variational weight optimization also makes it possible to customize the weights so that they preserve the shape of specified essential object features. We demonstrate successful use of our blending weights for real-time deformation of 2D and 3D shapes.

A physics-based control system that tracks a single motion trajectory produces high-quality animations, but does not recover from large disturbances that require deviating from this tracked trajectory. In order to enhance the responsiveness of physically simulated characters, we introduce algorithms that construct composite controllers that track multiple trajectories in parallel instead of sequentially switching from one control to the other. The composite controllers can blend or transition between different path controllers at arbitrary times according to the current system state. As a result, a composite control system generates both high-quality animations and natural responses to certain disturbances. We demonstrate its potential for improving robustness in performing several locomotion tasks. Then we consolidate these controllers into graphs that allow us to direct the character in real time.

Abstract Clothoid splines are gaining popularity as a curve representation due to their intrinsically pleasing curvature, which varies piecewise linearly over arc length. However, constructing them from hand‐drawn strokes remains difficult. Building on recent results, we describe a novel algorithm for approximating a sketched stroke with a fair (i.e., visually pleasing) clothoid spline. Fairness depends on proper segmentation of the stroke into curve primitives — lines, arcs, and clothoids. Our main idea is to cast the segmentation as a shortest path problem on a carefully constructed weighted graph. The nodes in our graph correspond to a vastly overcomplete set of curve primitives that are fit to every subsegment of the sketch, and edges correspond to transitions of a specified degree of continuity between curve primitives. The shortest path in the graph corresponds to a desirable segmentation of the input curve. Once the segmentation is found, the primitives are fit to the curve using non‐linear constrained optimization. We demonstrate that the curves produced by our method have good curvature profiles, while staying close to the user sketch.

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.

Transferring existing mesh deformation from one character to another is a simple way to accelerate the laborious process of mesh animation. In many cases, it is useful to preserve the semantic characteristics of the motion instead of its literal deformation. For example, when applying the walking motion of a human to a flamingo, the knees should bend in the opposite direction. Semantic deformation transfer accomplishes this task with a shape space that enables interpolation and projection with standard linear algebra. Given several example mesh pairs, semantic deformation transfer infers a correspondence between the shape spaces of the two characters. This enables automatic transfer of new poses and animations.

Keyframe animation is a common technique to generate animations of deformable characters and other soft bodies. With spline interpolation, however, it can be difficult to achieve secondary motion effects such as plausible dynamics when there are thousands of degrees of freedom to animate. Physical methods can provide more realism with less user effort, but it is challenging to apply them to quickly create specific animations that closely follow prescribed animator goals. We present a fast space-time optimization method to author physically based deformable object simulations that conform to animator-specified keyframes. We demonstrate our method with FEM deformable objects and mass-spring systems. Our method minimizes an objective function that penalizes the sum of keyframe deviations plus the deviation of the trajectory from physics. With existing methods, such minimizations operate in high dimensions, are slow, memory consuming, and prone to local minima. We demonstrate that significant computational speedups and robustness improvements can be achieved if the optimization problem is properly solved in a low-dimensional space. Selecting a low-dimensional space so that the intent of the animator is accommodated, and that at the same time space-time optimization is convergent and fast, is difficult. We present a method that generates a quality low-dimensional space using the given keyframes. It is then possible to find quality solutions to difficult space-time optimization problems robustly and in a manner of minutes.

Dynamically simulated characters are difficult to control because they are underactuated---they have no direct control over their global position and orientation. In order to succeed, control policies must look ahead to determine stabilizing actions, but such planning is complicated by frequent ground contacts that produce a discontinuous search space. This paper introduces a locomotion system that generates high-quality animation of agile movements using nonlinear controllers that plan through such contact changes. We demonstrate the general applicability of this approach by emulating walking and running motions in rigid-body simulations. Then we consolidate these controllers under a higher-level planner that interactively controls the character's direction.

Controllers are necessary for physically-based synthesis of character animation. However, creating controllers requires either manual tuning or expensive computer optimization. We introduce linear Bellman combination as a method for reusing existing controllers. Given a set of controllers for related tasks, this combination creates a controller that performs a new task. It naturally weights the contribution of each component controller by its relevance to the current state and goal of the system. We demonstrate that linear Bellman combination outperforms naive combination often succeeding where naive combination fails. Furthermore, this combination is provably optimal for a new task if the component controllers are also optimal for related tasks. We demonstrate the applicability of linear Bellman combination to interactive character control of stepping motions and acrobatic maneuvers.

We describe a system for high-resolution capture of moving 3D geometry, beginning with dynamic normal maps from multiple views. The normal maps are captured using active shape-from-shading (photometric stereo), with a large lighting dome providing a series of novel spherical lighting configurations. To compensate for low-frequency deformation, we perform multi-view matching and thin-plate spline deformation on the initial surfaces obtained by integrating the normal maps. Next, the corrected meshes are merged into a single mesh using a volumetric method. The final output is a set of meshes, which were impossible to produce with previous methods. The meshes exhibit details on the order of a few millimeters, and represent the performance over human-size working volumes at a temporal resolution of 60Hz.

Articulated hand-tracking systems have been widely used in virtual reality but are rarely deployed in consumer applications due to their price and complexity. In this paper, we propose an easy-to-use and inexpensive system that facilitates 3-D articulated user-input using the hands. Our approach uses a single camera to track a hand wearing an ordinary cloth glove that is imprinted with a custom pattern. The pattern is designed to simplify the pose estimation problem, allowing us to employ a nearest-neighbor approach to track hands at interactive rates. We describe several proof-of-concept applications enabled by our system that we hope will provide a foundation for new interactions in modeling, animation control and augmented reality.

In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data sets this is inappropriate. For example, in human motion data we expect latent spaces that are cylindrical or a toroidal, that are poorly captured with a Euclidean space. In this paper, we present a range of approaches for embedding data in a non-Euclidean latent space. Our focus is the Gaussian Process latent variable model. In the context of human motion modeling this allows us to (a) learn models with interpretable latent directions enabling, for example, style/content separation, and (b) generalise beyond the data set enabling us to learn transitions between motion styles even though such transitions are not present in the data.

Abstract Many data‐driven animation techniques are capable of producing high quality motions of human characters. Few techniques, however, are capable of generating motions that are consistent with physically simulated environments. Physically simulated characters, in contrast, are automatically consistent with the environment, but their motions are often unnatural because they are difficult to control. We present a model‐predictive controller that yields natural motions by guiding simulated humans toward real motion data. During simulation, the predictive component of the controller solves a quadratic program to compute the forces for a short window of time into the future. These forces are then applied by a low‐gain proportional‐derivative component, which makes minor adjustments until the next planning cycle. The controller is fast enough for interactive systems such as games and training simulations. It requires no precomputation and little manual tuning. The controller is resilient to mismatches between the character dynamics and the input motion, which allows it to track motion capture data even where the real dynamics are not known precisely. The same principled formulation can generate natural walks, runs, and jumps in a number of different physically simulated surroundings.

Recent advances have brought real-time physically based simulation within reach, but simulations are still difficult to control in real time. We present interactive simulations of passive systems such as deformable solids or fluids that are not only fast, but also directable: they follow given input trajectories while simultaneously reacting to user input and other unexpected disturbances. We achieve such directability using a real-time controller that runs in tandem with a real-time physically based simulation. To avoid stiff and over-controlled systems where the natural dynamics are overpowered, the injection of control forces has to be minimized. This search for gentle forces can be made tractable in real-time by linearizing the system dynamics around the input trajectory, and then using a time-varying linear quadratic regulator to build the controller. We show examples of controlled complex deformable solids and fluids, demonstrating that our approach generates a requested fixed outcome for reasonable user inputs, while simultaneously providing runtime motion variety.

Animating natural human motion in dynamic environments is difficult because of complex geometric and physical interactions. Simulation provides an automatic solution to parts of this problem, but it needs control systems to produce lifelike motions. This paper describes the systematic computation of controllers that can reproduce a range of locomotion styles in interactive simulations. Given a reference motion that describes the desired style, a derived control system can reproduce that style in simulation and in new environments. Because it produces high-quality motions that are both geometrically and physically consistent with simulated surroundings, interactive animation systems could begin to use this approach along with more established kinematic methods.

Details in mesh animations are difficult to generate but they have great impact on visual quality. In this work, we demonstrate a practical software system for capturing such details from multi-view video recordings. Given a stream of synchronized video images that record a human performance from multiple viewpoints and an articulated template of the performer, our system captures the motion of both the skeleton and the shape. The output mesh animation is enhanced with the details observed in the image silhouettes. For example, a performance in casual loose-fitting clothes will generate mesh animations with flowing garment motions. We accomplish this with a fast pose tracking method followed by nonrigid deformation of the template to fit the silhouettes. The entire process takes less than sixteen seconds per frame and requires no markers or texture cues. Captured meshes are in full correspondence making them readily usable for editing operations including texturing, deformation transfer, and deformation model learning.

Animating an articulated 3D character currently requires manual rigging to specify its internal skeletal structure and to define how the input motion deforms its surface. We present a method for animating characters automatically. Given a static character mesh and a generic skeleton, our method adapts the skeleton to the character and attaches it to the surface, allowing skeletal motion data to animate the character. Because a single skeleton can be used with a wide range of characters, our method, in conjunction with a library of motions for a few skeletons, enables a user-friendly animation system for novices and children. Our prototype implementation, called Pinocchio, typically takes under a minute to rig a character on a modern midrange PC.

Commercial motion-capture systems produce excellent in-studio reconstructions, but offer no comparable solution for acquisition in everyday environments. We present a system for acquiring motions almost anywhere. This wearable system gathers ultrasonic time-of-flight and inertial measurements with a set of inexpensive miniature sensors worn on the garment. After recording, the information is combined using an Extended Kalman Filter to reconstruct joint configurations of a body. Experimental results show that even motions that are traditionally difficult to acquire are recorded with ease within their natural settings. Although our prototype does not reliably recover the global transformation, we show that the resulting motions are visually similar to the original ones, and that the combined acoustic and intertial system reduces the drift commonly observed in purely inertial systems. Our final results suggest that this system could become a versatile input device for a variety of augmented-reality applications.

Enveloping, or the mapping of skeletal controls to the deformations of a surface, is key to driving realistic animated characters. Despite its widespread use, enveloping still relies on slow or inaccurate deformation methods. We propose a method that is both fast, accurate and example-based. Our technique introduces a rotational regression model that captures common skinning deformations such as muscle bulging, twisting, and challenging areas such as the shoulders. Our improved treatment of rotational quantities is made practical by model reduction that ensures real-time solution of least-squares problems, independent of the mesh size. Our method is significantly more accurate than linear blend skinning and almost as fast, suggesting its use as a replacement for linear blend skinning when examples are available.

Articulated shapes are aptly described by reduced deformable models that express required shape deformations using a compact set of control parameters. Although sufficient to describe most shape deformations, these control parameters can be ill-suited for animation tasks, particularly when reduced deformable models are inferred automatically from example shapes. Our algorithm provides intuitive and direct control of reduced deformable models similar to a conventional inverse-kinematics algorithm for jointed rigid structures. We present a fully automated pipeline that transforms a set of unarticulated example shapes into a controllable, articulated model. With only a few manipulations, an animator can automatically and interactively pose detailed shapes at rates independent of their geometric complexity.

Style translation is the process of transforming an input motion into a new style while preserving its original content. This problem is motivated by the needs of interactive applications, which require rapid processing of captured performances. Our solution learns to translate by analyzing differences between performances of the same content in input and output styles. It relies on a novel correspondence algorithm to align motions, and a linear time-invariant model to represent stylistic differences. Once the model is estimated with system identification, our system is capable of translating streaming input with simple linear operations at each frame.

Adaptation of ballistic motion demands a technique that can make required adjustments in anticipation of flight periods when only some physically consistent changes are possible. This article describes a numerical procedure that adjusts a physically consistent motion to fulfill new adaptation requirements expressed in kinematic and dynamic constraints. This iterative procedure refines the original motion with a sequence of minimal adjustments, implicitly favoring motions that are similar to the original performance, and transforming any input motion, including those that are difficult to characterize with an objective function. In total, over twenty adaptations were generated from two recorded performances, a run and a jump, by varying foot placement, restricting muscle use, adding new environment constraints, and changing the length and mass of specific limbs.

The ability to position a small subset of mesh vertices and produce a meaningful overall deformation of the entire mesh is a fundamental task in mesh editing and animation. However, the class of meaningful deformations varies from mesh to mesh and depends on mesh kinematics, which prescribes valid mesh configurations, and a selection mechanism for choosing among them. Drawing an analogy to the traditional use of skeleton-based inverse kinematics for posing skeletons. we define mesh-based inverse kinematics as the problem of finding meaningful mesh deformations that meet specified vertex constraints.Our solution relies on example meshes to indicate the class of meaningful deformations. Each example is represented with a feature vector of deformation gradients that capture the affine transformations which individual triangles undergo relative to a reference pose. To pose a mesh, our algorithm efficiently searches among all meshes with specified vertex positions to find the one that is closest to some pose in a nonlinear span of the example feature vectors. Since the search is not restricted to the span of example shapes, this produces compelling deformations even when the constraints require poses that are different from those observed in the examples. Furthermore, because the span is formed by a nonlinear blend of the example feature vectors, the blending component of our system may also be used independently to pose meshes by specifying blending weights or to compute multi-way morph sequences.

Face Transfer is a method for mapping videorecorded performances of one individual to facial animations of another. It extracts visemes (speech-related mouth articulations), expressions, and three-dimensional (3D) pose from monocular video or film footage. These parameters are then used to generate and drive a detailed 3D textured face mesh for a target identity, which can be seamlessly rendered back into target footage. The underlying face model automatically adjusts for how the target performs facial expressions and visemes. The performance data can be easily edited to change the visemes, expressions, pose, or even the identity of the target---the attributes are separably controllable. This supports a wide variety of video rewrite and puppetry applications.Face Transfer is based on a multilinear model of 3D face meshes that separably parameterizes the space of geometric variations due to different attributes (e.g., identity, expression, and viseme). Separability means that each of these attributes can be independently varied. A multilinear model can be estimated from a Cartesian product of examples (identities × expressions × visemes) with techniques from statistical analysis, but only after careful preprocessing of the geometric data set to secure one-to-one correspondence, to minimize cross-coupling artifacts, and to fill in any missing examples. Face Transfer offers new solutions to these problems and links the estimated model with a face-tracking algorithm to extract pose, expression, and viseme parameters.

Deformation transfer applies the deformation exhibited by a source triangle mesh onto a different target triangle mesh. Our approach is general and does not require the source and target to share the same number of vertices or triangles, or to have identical connectivity. The user builds a correspondence map between the triangles of the source and those of the target by specifying a small set of vertex markers. Deformation transfer computes the set of transformations induced by the deformation of the source mesh, maps the transformations through the correspondence from the source to the target, and solves an optimization problem to consistently apply the transformations to the target shape. The resulting system of linear equations can be factored once, after which transferring a new deformation to the target mesh requires only a backsubstitution step. Global properties such as foot placement can be achieved by constraining vertex positions. We demonstrate our method by retargeting full body key poses, applying scanned facial deformations onto a digital character, and remapping rigid and non-rigid animation sequences from one mesh onto another.

Motion sketching is an approach for creating realistic rigid-body motion. In this approach, an animator sketches how objects should move and the system computes a physically plausible motion that best fits the sketch. The sketch is specified with a mouse-based interface or with hand-gestures, which move instrumented objects in the real world to act out the desired behaviors. The sketches may be imprecise, may be physically infeasible, or may have incorrect timing. A multiple-shooting optimization estimates the parameters of a rigid-body simulation needed to simulate an animation that matches the sketch with physically plausible timing and motion. This technique applies to physical simulations of multiple colliding rigid bodies possibly connected with joints in a tree (open-loop) topology.

We describe a method for the acquisition of deformable human geometry from silhouettes. Our technique uses a commercial tracking system to determine the motion of the skeleton, then estimates geometry for each bone using constraints provided by the silhouettes from one or more cameras. These silhouettes do not give a complete characterization of the geometry for a particular point in time, but when the subject moves, many observations of the same local geometries allow the construction of a complete model. Our reconstruction algorithm provides a simple mechanism for solving the problems of view aggregation, occlusion handling, hole filling, noise removal, and deformation modeling. The resulting model is parameterized to synthesize geometry for new poses of the skeleton. We demonstrate this capability by rendering the geometry for motion sequences that were not included in the original datasets.

In this research, an "intelligent" isolated intersection control system is developed. This paper proposes a two-step process that develops the rules of fuzzy control. In the first step, the best sequence of phases is developed for many different patterns of vehicle arrivals. These sequences are developed using a genetic algorithm approach. In the second step, learning from the best strategies, specific rules are defined. The uniqueness of the proposed approach is that the rules are derived from the set of chosen examples assuming that the future traffic arrival patterns are known. This approach also allows a better evaluation of the performance of the fuzzy control because the best solution developed in the first step is used as the reference. The results were found to be very close to the best solution assuming that the future arrival pattern is known.

Physical simulation of dynamic objects has become commonplace in computer graphics because it produces highly realistic animations. In this paradigm the animator provides few physical parameters such as the objects' initial positions and velocities, and the simulator automatically generates realistic motions. The resulting motion, however, is difficult to control because even a small adjustment of the input parameters can drastically affect the subsequent motion. Furthermore, the animator often wishes to change the end-result of the motion instead of the initial physical parameters.

In this paper, we introduce the progressive simplicial complex (PSC) representation, a new format for storing and transmitting triangulated geometric models. Like the earlier progressive mesh (PM) representation, it captures a given model as a coarse base model together with a sequence of refinement transformations that progressively recover detail. The PSC representation makes use of a more general refinement transformation, allowing the given model to be an arbitrary triangulation (e.g. any dimension, non-orientable, non-manifold, non-regular), and the base model to always consist of a single vertex. Indeed, the sequence of refinement transformations encodes both the geometry and the topology of the model in a unified multiresolution framework. The PSC representation retains the advantages of PM’s. It defines a continuous sequence of approximating models for runtime level-of-detail control, allows smooth transitions between any pair of models in the sequence, supports progressive transmission, and offers a space-efficient representation. Moreover, by allowing changes to topology, the PSC sequence of approximations achieves better fidelity than the corresponding PM sequence. We develop an optimization algorithm for constructing PSC representations for graphics surface models, and demonstrate the framework on models that are both geometrically and topologically complex.

Multiresolution analysis has been proposed as a basic tool supporting compression, progressive transmission, and level-of-detail control of complex meshes in a unified and theoretically sound way. We extend previous work on multiresolution analysis of meshes in two ways. First, we show how to perform multiresolution analysis of colored meshes by separately analyzing shape and color. Second, we describe efficient algorithms and data structures that allow us to incrementally construct lower resolution approximations to colored meshes from the geometry and color wavelet coefficients at interactive rates. We have integrated these algorithms in a prototype mesh viewer that supports progressive transmission, dynamic display at a constant frame rate independent of machine characteristics and load, and interactive choice of tradeoff between the amount of detail in geometry and color. The viewer operates as a helper application to Netscape, and can therefore be used to rapidly browse and di...