Motion capture is increasingly used in games and movies, but often requires editing before it can be used, for many reasons. The motion may need to be adjusted to correctly interact with virtual objects or to fix problems that result from mapping the motion to a character of a different size or, beyond such technical requirements, directors can request stylistic changes. Unfortunately, editing is laborious because of the low-level representation of the data. While existing motion editing methods accomplish modest changes, larger edits can require the artist to “re-animate” the motion by manually selecting a subset of the frames as keyframes. In this paper, we automatically find sets of frames to serve as keyframes for editing the motion. We formulate the problem of selecting an optimal set of keyframes as a shortest-path problem, and solve it efficiently using dynamic programming. We create a new simplified animation by interpolating the found keyframes using a naive curve fitting technique. Our algorithm can simplify motion capture to around 10% of the original number of frames while retaining most of its detail. By simplifying animation with our algorithm, we realize a new approach to motion editing and stylization founded on the time-tested keyframe interface. We present results that show our algorithm outperforms both research algorithms and a leading commercial tool.

From angling smiles to duck faces, all kinds of facial expressions can be seen in selfies, portraits, and Internet pictures. These photos are taken from various camera types, and under a vast range of angles and lighting conditions. We present a deep learning framework that can fully normalize unconstrained face images, i.e., remove perspective distortions, relight to an evenly lit environment, and predict a frontal and neutral face. Our method can produce a high resolution image while preserving important facial details and the likeness of the subject, along with the original background. We divide this ill-posed problem into three consecutive normalization steps, each using a different generative adversarial network that acts as an image generator. Perspective distortion removal is performed using a dense flow field predictor. A uniformly illuminated face is obtained using a lighting translation network, and the facial expression is neutralized using a generalized facial expression synthesis framework combined with a regression network based on deep features for facial recognition. We introduce new data representations for conditional inference, as well as training methods for supervised learning to ensure that different expressions of the same person can yield to not only a plausible but also a similar neutral face. We demonstrate our results on a wide range of challenging images collected in the wild. Key applications of our method range from robust image-based 3D avatar creation, portrait manipulation, to facial enhancement and reconstruction tasks for crime investigation. We also found through an extensive user study, that our normalization results can be hardly distinguished from ground truth ones if the person is not familiar.

A circuit operating at near-threshold voltage (NTV) dissipates much less energy, but it suffers from significant increase in cell delay as well as delay variation. In this paper, we address two library optimization methods for NTV design: (1) transistor lengths are increased to benefit from reverse short channel effect (RSCE), and (2) each flip-flop is optimized into a few versions with different timing parameters by redistributing clock signals to clocked transistors. Flip-flops are remapped to optimized ones via integer linear programming (ILP); a goal is to minimize total negative slack (TNS) under hold time constraints, which is a critical concern in NTV design. Experiments demonstrate that our proposed method achieves 22%, 56%, and 13% reductions in clock period, energy dissipation, and circuit area, respectively, on average of a few test circuits in 55-nm technology.

With the rising interest in personalized VR and gaming experiences comes the need to create high quality 3D avatars that are both low-cost and variegated. Due to this, building dynamic avatars from a single unconstrained input image is becoming a popular application. While previous techniques that attempt this require multiple input images or rely on transferring dynamic facial appearance from a source actor, we are able to do so using only one 2D input image without any form of transfer from a source image. We achieve this using a new conditional Generative Adversarial Network design that allows fine-scale manipulation of any facial input image into a new expression while preserving its identity. Our photoreal avatar GAN (paGAN) can also synthesize the unseen mouth interior and control the eye-gaze direction of the output, as well as produce the final image from a novel viewpoint. The method is even capable of generating fully-controllable temporally stable video sequences, despite not using temporal information during training. After training, we can use our network to produce dynamic image-based avatars that are controllable on mobile devices in real time. To do this, we compute a fixed set of output images that correspond to key blendshapes, from which we extract textures in UV space. Using a subject's expression blendshapes at run-time, we can linearly blend these key textures together to achieve the desired appearance. Furthermore, we can use the mouth interior and eye textures produced by our network to synthesize on-the-fly avatar animations for those regions. Our work produces state-of-the-art quality image and video synthesis, and is the first to our knowledge that is able to generate a dynamically textured avatar with a mouth interior, all from a single image.

The layout of standard cells is very dense these days, so some pins are hard to get access to. This is in particular true in complex cells with many pins (e.g. AOI) and in the layout where many of those cells are densely packed without much whitespace. We redesign those complex cells, so a library now contains both original cell and its new version with easier pin access; a systematic method is proposed to pick candidate cells for redesign and to dictate how redesign should be performed. We also introduce a measure of inaccessibility of pins in a cell, named IOC. Placement optimization is performed, which uses IOC to determine which cells should be replaced by its redesigned version and how whitespace should be redistributed. Experiments with 12 test circuits indicate that the number of routing errors (after the initial placement) is reduced by 82% on average, and the subsequent detailed routing takes 72% less runtime.

We present a fully automatic framework that digitizes a complete 3D head with hair from a single unconstrained image. Our system offers a practical and consumer-friendly end-to-end solution for avatar personalization in gaming and social VR applications. The reconstructed models include secondary components (eyes, teeth, tongue, and gums) and provide animation-friendly blendshapes and joint-based rigs. While the generated face is a high-quality textured mesh, we propose a versatile and efficient polygonal strips (polystrips) representation for the hair. Polystrips are suitable for an extremely wide range of hairstyles and textures and are compatible with existing game engines for real-time rendering. In addition to integrating state-of-the-art advances in facial shape modeling and appearance inference, we propose a novel single-view hair generation pipeline, based on 3D-model and texture retrieval, shape refinement, and polystrip patching optimization. The performance of our hairstyle retrieval is enhanced using a deep convolutional neural network for semantic hair attribute classification. Our generated models are visually comparable to state-of-the-art game characters designed by professional artists. For real-time settings, we demonstrate the flexibility of polystrips in handling hairstyle variations, as opposed to conventional strand-based representations. We further show the effectiveness of our approach on a large number of images taken in the wild, and how compelling avatars can be easily created by anyone.

The goal of a practical facial animation retargeting system is to reproduce the character of a source animation on a target face while providing room for additional creative control by the animator. This article presents a novel spacetime facial animation retargeting method for blendshape face models. Our approach starts from the basic principle that the source and target movements should be similar. By interpreting movement as the derivative of position with time, and adding suitable boundary conditions, we formulate the retargeting problem as a Poisson equation. Specified (e.g., neutral) expressions at the beginning and end of the animation as well as any user-specified constraints in the middle of the animation serve as boundary conditions. In addition, a model-specific prior is constructed to represent the plausible expression space of the target face during retargeting. A Bayesian formulation is then employed to produce target animation that is consistent with the source movements while satisfying the prior constraints. Since the preservation of temporal derivatives is the primary goal of the optimization, the retargeted motion preserves the rhythm and character of the source movement and is free of temporal jitter. More importantly, our approach provides spacetime editing for the popular blendshape representation of facial models, exhibiting smooth and controlled propagation of user edits across surrounding frames.

We present a method to compress complex blendshape models and thereby enable interactive, hardware-accelerated animation of these models. Facial blendshape models in production are typically large in terms of both the resolution of the model and the number of target shapes. They are represented by a single huge blendshape matrix, whose size presents a storage burden and prevents real-time processing. To address this problem, we present a new matrix compression scheme based on a hierarchically semi-separable (HSS) representation with matrix block reordering. The compressed data are also suitable for parallel processing. An efficient GPU implementation provides very fast feedback of the resulting animation. Compared with the original data, our technique leads to a huge improvement in both storage and processing efficiency without incurring any visual artifacts. As an application, we introduce an extended version of the direct manipulation method to control a large number of facial blendshapes efficiently and intuitively.

This paper presents a novel facial animation retargeting system that is carefully designed to support the animator's workflow. Observation and analysis of the animators' often preferred process of key-frame animation with blendshape models informed our research. Our retargeting system generates a similar set of blendshape weights to those that would have been produced by an animator. This is achieved by rearranging the group of blendshapes into several sequential retargeting groups and solving using a matching pursuit-like scheme inspired by a traditional key-framing approach. Meanwhile, animators typically spend a tremendous amount of time simplifying the dense weight graphs created by the retargeting. Our graph simplification technique effectively produces editable weight graphs while preserving the visual characteristics of the original retargeting. Finally, we automatically create GUI controllers to help artists perform key-framing and editing very efficiently. The set of proposed techniques greatly reduce the time and effort required by animators to achieve high quality retargeted facial animations.

Abstract Realistic character animation requires elaborate rigging built on top of high quality 3D models. Sophisticated anatomically based rigs are often the choice of visual effect studios where life‐like animation of CG characters is the primary objective. However, rigging a character with a muscular‐skeletal system is very involving and time‐consuming process, even for professionals. Although, there have been recent research efforts to automate either all or some parts of the rigging process, the complexity of anatomically based rigging nonetheless opens up new research challenges. We propose a new method to automate anatomically based rigging that transfers an existing rig of one character to another. The method is based on a data interpolation in the surface and volume domain, where various rigging elements can be transferred between different models. As it only requires a small number of corresponding input feature points, users can produce highly detailed rigs for a variety of desired character with ease. Copyright © 2010 John Wiley & Sons, Ltd.