Abstract Facial animation is one of the most labor‐intensive aspects of animation and VFX, as traditional rigging consumes weeks of expert time and forces animators to spend countless hours manipulating hundreds of controls to achieve varied expressions. This technical complexity creates a barrier between artistic vision and execution, limiting creative exploration and iteration. In this paper, we introduce CANRig , a fully automated neural facial rigging approach that simplifies the process of creating and editing facial poses by benefiting from global correlations learned from data. Unlike existing neural face models that either sacrifice local control or demand extensive manual region setup, our method introduces continuous local control through a novel conditioning mechanism that operates on a variable region. By modeling deformation as cross‐attention between control handles and mesh vertices—modulated by a user‐defined region—we enable seamless transitions from precise local adjustments to broad global changes. We further expand our method with a shape‐preserving workflow that enables iterative edits, guaranteeing that changes remain untouched even as controls are reconfigured. Our method delivers the best of both worlds: the automation and naturalness of neural methods with the granular control that professional animators demand, and we demonstrate its effectiveness across multiple applications in both animation and high‐end visual effects pipelines.

Generating realistic facial animation for CG characters and digital doubles is one of the hardest tasks in animation. A typical production workflow involves capturing the performance of a real actor using mo-cap technology, and transferring the captured motion to the target digital character. This process, known as retargeting , has been used for over a decade, and typically relies on either large blendshape rigs that are expensive to create, or direct deformation transfer algorithms that operate on individual geometric elements and are prone to artifacts. We present a new method for high-fidelity offline facial performance retargeting that is neither expensive nor artifact-prone. Our two step method first transfers local expression details to the target, and is followed by a global face surface prediction that uses anatomical constraints in order to stay in the feasible shape space of the target character. Our method also offers artists with familiar blendshape controls to perform fine adjustments to the retargeted animation. As such, our method is ideally suited for the complex task of human-to-human 3D facial performance retargeting, where the quality bar is extremely high in order to avoid the uncanny valley, while also being applicable for more common human-to-creature settings. We demonstrate the superior performance of our method over traditional deformation transfer algorithms, while achieving a quality comparable to current blendshape-based techniques used in production while requiring significantly fewer input shapes at setup time. A detailed user study corroborates the realistic and artifact free animations generated by our method in comparison to existing techniques.

Character animation tools are based on a keyframing metaphor where artists pose characters at selected keyframes and the software automatically interpolates the frames inbetween. Although the quality of the interpolation is critical for achieving a fluid and engaging animation, the tools available to adjust the result of the automatic inbetweening are rudimentary and typically require manual editing of spline parameters. As a result, artists spend a tremendous amount of time posing and setting more keyframes. In this pose-centric workflow, animators use combinations of forward and inverse kinematics. While forward kinematics leads to intuitive interpolations, it does not naturally support positional constraints such as fixed contact points. Inverse kinematics can be used to fix certain points in space at keyframes, but can lead to inferior interpolations, is slow to compute, and does not allow for positional contraints at non-keyframe frames. In this paper, we address these problems by formulating the control of interpolations with positional constraints over time as a space-time optimization problem in the tangent space of the animation curves driving the controls. Our method has the key properties that it (1) allows the manipulation of positions and orientations over time, extending inverse kinematics, (2) does not add new keyframes that might conflict with an artist's preferred keyframe style, and (3) works in the space of artist editable animation curves and hence integrates seamlessly with current pipelines. We demonstrate the utility of the technique in practice via various examples and use cases.

Video games enable the representation and control of characters that can agilely evolve in virtual environments. However, the detached character interaction they propose - often using a push-button metaphor - is far from the satisfactory feeling of grasping and moving physical toys. In this paper, we propose a new interaction metaphor that reduces the gap between physical toys and virtual characters. The user moves a smartphone around, and a puppet that responds in real time to the manipulations is seen through the screen. The virtual character moves in order to follow the user gestures, as if it was attached to the phone via a rigid stick. This yields a natural interaction, similar to moving a physical toy, and the puppet now feels alive because its movements are augmented with compelling animations. Using the smartphone, our method ties together the control of the character and camera into a single interaction mechanism. We validate our system by presenting an application in Augmented Reality.

Abstract Effective composition in visual arts relies on the principle of movement, where the viewer's eye is directed along subjective curves to a center of interest. We call these curves subjective because they may span the edges and/or center‐lines of multiple objects, as well as contain missing portions which are automatically filled by our visual system. By carefully coordinating the shape of objects in a scene, skilled artists direct the viewer's attention via strong subjective curves. While traditional 2D sketching is a natural fit for this task, current 3D tools are object‐centric and do not accommodate coherent deformation of multiple shapes into smooth flows. We address this shortcoming with a new sketch‐based interface called Flow Curves which allows coordinating deformation across multiple objects. Core components of our method include an understanding of the principle of flow, algorithms to automatically identify subjective curve elements that may span multiple disconnected objects, and a deformation representation tailored to the view‐dependent nature of scene movement. As demonstrated in our video, sketching flow curves requires significantly less time than using traditional 3D editing workflows.