We present a real-time solution for generating detailed clothing deformations from pre-computed clothing shape examples. Given an input pose, it synthesizes a clothing deformation by blending skinned clothing deformations of nearby examples controlled by the body skeleton. Observing that cloth deformation can be well modeled with sensitivity analysis driven by the underlying skeleton, we introduce a sensitivity based method to construct a pose-dependent rigging solution from sparse examples. We also develop a sensitivity based blending scheme to find nearby examples for the input pose and evaluate their contributions to the result. Finally, we propose a stochastic optimization based greedy scheme for sampling the pose space and generating example clothing shapes. Our solution is fast, compact and can generate realistic clothing animation results for various kinds of clothes in real time.

Feature matters for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present Progressive Feature Polishing Network (PFPN), a simple yet effective framework to progressively polish the multi-level features to be more accurate and representative. By employing multiple Feature Polishing Modules (FPMs) in a recurrent manner, our approach is able to detect salient objects with fine details without any post-processing. A FPM parallelly updates the features of each level by directly incorporating all higher level context information. Moreover, it can keep the dimensions and hierarchical structures of the feature maps, which makes it flexible to be integrated with any CNN-based models. Empirical experiments show that our results are monotonically getting better with increasing number of FPMs. Without bells and whistles, PFPN outperforms the state-of-the-art methods significantly on five benchmark datasets under various evaluation metrics. Our code is available at: https://github.com/chenquan-cq/PFPN.

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Figure 1: Given (a) a single portrait image and a few user strokes, we generated (b) a high quality 3D hair model whose visual fidelity and physical plausibility enabled several dynamic hair manipulating applications, such as (c) physically-based simulation, (d) combing, or (e,f) motion-preserving hair replacement in video. Original images courtesy of Asian Impressions Photography. AbstractThis paper presents a single-view hair modeling technique for generating visually and physically plausible 3D hair models with modest user interaction. By solving an unambiguous 3D vector field explicitly from the image and adopting an iterative hair generation algorithm, we can create hair models that not only visually match the original input very well but also possess physical plausibility (e.g., having strand roots fixed on the scalp and preserving the length and continuity of real strands in the image as much as possible). The latter property enables us to manipulate hair in many new ways that were previously very difficult with a single image, such as dynamic simulation or interactive hair shape editing. We further extend the modeling approach to handle simple video input, and generate dynamic 3D hair models. This allows users to manipulate hair in a video or transfer styles from images to videos.

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Figure 1: From an input sequence (bottom) of a standing T. rex simulated via a Saint Venant-Kirchhoff deformation model, we edit the input motion with 27 space-time constraints to make the dinosaur squat & jump while trying to catch the small plane flying around it (top). AbstractWe present a novel method for elastic animation editing with spacetime constraints. In a sharp departure from previous approaches, we not only optimize control forces added to a linearized dynamic model, but also optimize material properties to better match user constraints and provide plausible and consistent motion. Our approach achieves efficiency and scalability by performing all computations in a reduced rotation-strain (RS) space constructed with both cubature and geometric reduction, leading to two orders of magnitude improvement over the original RS method. We demonstrate the utility and versatility of our method in various applications, including motion editing, pose interpolation, and estimation of material parameters from existing animation sequences.

We present an engine for enhancing the geometry of a 3D face mesh model while making the enhanced version share close similarity with the original. After obtaining the feature points of a given scanned 3D face model, we first perform a local and global symmetrization on the key facial features. We then apply an overall proportion optimization to the frontal face based on Neoclassical Canons and golden ratios. A nonlinear least-squares solution is adopted to adjust the feature points so that the face profile complies with the aesthetic criteria, which are derived from the profile cosmetology. Through the above processes, we obtain the optimized feature points, which will lead to a more attractive face. According to the original feature points and the optimized ones, we perform Laplacian deformation to adjust the remaining points of the face in order to preserve the geometric details. The analysis of user study in this paper validates the effectiveness of our 3D face geometry enhancement engine.