Fast character modeling with sketch-based PDE surfaces

© 2020, The Author(s). Virtual characters are 3D geometric models of characters. They have a lot of applications in multimedia. In this paper, we propose a new physics-based deformation method and efficient character modelling framework for creation of detailed 3D virtual character models. Our proposed physics-based deformation method uses PDE surfaces. Here PDE is the abbreviation of Partial Differential Equation, and PDE surfaces are defined as sculpting force-driven shape representations of interpolation surfaces. Interpolation surfaces are obtained by interpolating key cross-section profile curves and the sculpting force-driven shape representation uses an analytical solution to a vector-valued partial differential equation involving sculpting forces to quickly obtain deformed shapes. Our proposed character modelling framework consists of global modeling and local modeling. The global modeling is also called model building, which is a process of creating a whole character model quickly with sketch-guided and template-based modeling techniques. The local modeling produces local details efficiently to improve the realism of the created character model with four shape manipulation techniques. The sketch-guided global modeling generates a character model from three different levels of sketched profile curves called primary, secondary and key cross-section curves in three orthographic views. The template-based global modeling obtains a new character model by deforming a template model to match the three different levels of profile curves. Four shape manipulation techniques for local modeling are investigated and integrated into the new modelling framework. They include: partial differential equation-based shape manipulation, generalized elliptic curve-driven shape manipulation, sketch assisted shape manipulation, and template-based shape manipulation. These new local modeling techniques have both global and local shape control functions and are efficient in local shape manipulation. The final character models are represented with a collection of surfaces, which are modeled with two types of geometric entities: generalized elliptic curves (GECs) and partial differential equation-based surfaces. Our experiments indicate that the proposed modeling approach can build detailed and realistic character models easily and quickly

3D hand tracking.

The hand is often considered as one of the most natural and intuitive interaction modalities for human-to-human interaction. In human-computer interaction (HCI), proper 3D hand tracking is the first step in developing a more intuitive HCI system which can be used in applications such as gesture recognition, virtual object manipulation and gaming. However, accurate 3D hand tracking, remains a challenging problem due to the hand’s deformation, appearance similarity, high inter-finger occlusion and complex articulated motion. Further, 3D hand tracking is also interesting from a theoretical point of view as it deals with three major areas of computer vision- segmentation (of hand), detection (of hand parts), and tracking (of hand). This thesis proposes a region-based skin color detection technique, a model-based and an appearance-based 3D hand tracking techniques to bring the human-computer interaction applications one step closer. All techniques are briefly described below. Skin color provides a powerful cue for complex computer vision applications. Although skin color detection has been an active research area for decades, the mainstream technology is based on individual pixels. This thesis presents a new region-based technique for skin color detection which outperforms the current state-of-the-art pixel-based skin color detection technique on the popular Compaq dataset (Jones & Rehg 2002). The proposed technique achieves 91.17% true positive rate with 13.12% false negative rate on the Compaq dataset tested over approximately 14,000 web images. Hand tracking is not a trivial task as it requires tracking of 27 degreesof- freedom of hand. Hand deformation, self occlusion, appearance similarity and irregular motion are major problems that make 3D hand tracking a very challenging task. This thesis proposes a model-based 3D hand tracking technique, which is improved by using proposed depth-foreground-background ii feature, palm deformation module and context cue. However, the major problem of model-based techniques is, they are computationally expensive. This can be overcome by discriminative techniques as described below. Discriminative techniques (for example random forest) are good for hand part detection, however they fail due to sensor noise and high interfinger occlusion. Additionally, these techniques have difficulties in modelling kinematic or temporal constraints. Although model-based descriptive (for example Markov Random Field) or generative (for example Hidden Markov Model) techniques utilize kinematic and temporal constraints well, they are computationally expensive and hardly recover from tracking failure. This thesis presents a unified framework for 3D hand tracking, using the best of both methodologies, which out performs the current state-of-the-art 3D hand tracking techniques. The proposed 3D hand tracking techniques in this thesis can be used to extract accurate hand movement features and enable complex human machine interaction such as gaming and virtual object manipulation

2D and 3D surface image processing algorithms and their applications

This doctoral dissertation work aims to develop algorithms for 2D image segmentation application of solar filament disappearance detection, 3D mesh simplification, and 3D image warping in pre-surgery simulation. Filament area detection in solar images is an image segmentation problem. A thresholding and region growing combined method is proposed and applied in this application. Based on the filament area detection results, filament disappearances are reported in real time. The solar images in 1999 are processed with this proposed system and three statistical results of filaments are presented. 3D images can be obtained by passive and active range sensing. An image registration process finds the transformation between each pair of range views. To model an object, a common reference frame in which all views can be transformed must be defined. After the registration, the range views should be integrated into a non-redundant model. Optimization is necessary to obtain a complete 3D model. One single surface representation can better fit to the data. It may be further simplified for rendering, storing and transmitting efficiently, or the representation can be converted to some other formats. This work proposes an efficient algorithm for solving the mesh simplification problem, approximating an arbitrary mesh by a simplified mesh. The algorithm uses Root Mean Square distance error metric to decide the facet curvature. Two vertices of one edge and the surrounding vertices decide the average plane. The simplification results are excellent and the computation speed is fast. The algorithm is compared with six other major simplification algorithms. Image morphing is used for all methods that gradually and continuously deform a source image into a target image, while producing the in-between models. Image warping is a continuous deformation of a: graphical object. A morphing process is usually composed of warping and interpolation. This work develops a direct-manipulation-of-free-form-deformation-based method and application for pre-surgical planning. The developed user interface provides a friendly interactive tool in the plastic surgery. Nose augmentation surgery is presented as an example. Displacement vector and lattices resulting in different resolution are used to obtain various deformation results. During the deformation, the volume change of the model is also considered based on a simplified skin-muscle model

SINE: Semantic-driven Image-based NeRF Editing with Prior-guided Editing Field

Despite the great success in 2D editing using user-friendly tools, such as Photoshop, semantic strokes, or even text prompts, similar capabilities in 3D areas are still limited, either relying on 3D modeling skills or allowing editing within only a few categories. In this paper, we present a novel semantic-driven NeRF editing approach, which enables users to edit a neural radiance field with a single image, and faithfully delivers edited novel views with high fidelity and multi-view consistency. To achieve this goal, we propose a prior-guided editing field to encode fine-grained geometric and texture editing in 3D space, and develop a series of techniques to aid the editing process, including cyclic constraints with a proxy mesh to facilitate geometric supervision, a color compositing mechanism to stabilize semantic-driven texture editing, and a feature-cluster-based regularization to preserve the irrelevant content unchanged. Extensive experiments and editing examples on both real-world and synthetic data demonstrate that our method achieves photo-realistic 3D editing using only a single edited image, pushing the bound of semantic-driven editing in 3D real-world scenes. Our project webpage: https://zju3dv.github.io/sine/.Comment: Accepted to CVPR 2023. Project Page: https://zju3dv.github.io/sine

Exoskeleton-covered soft finger with vision-based proprioception and tactile sensing

Soft robots offer significant advantages in adaptability, safety, and dexterity compared to conventional rigid-body robots. However, it is challenging to equip soft robots with accurate proprioception and tactile sensing due to their high flexibility and elasticity. In this work, we describe the development of a vision-based proprioceptive and tactile sensor for soft robots called GelFlex, which is inspired by previous GelSight sensing techniques. More specifically, we develop a novel exoskeleton-covered soft finger with embedded cameras and deep learning methods that enable high-resolution proprioceptive sensing and rich tactile sensing. To do so, we design features along the axial direction of the finger, which enable high-resolution proprioceptive sensing, and incorporate a reflective ink coating on the surface of the finger to enable rich tactile sensing. We design a highly underactuated exoskeleton with a tendon-driven mechanism to actuate the finger. Finally, we assemble 2 of the fingers together to form a robotic gripper and successfully perform a bar stock classification task, which requires both shape and tactile information. We train neural networks for proprioception and shape (box versus cylinder) classification using data from the embedded sensors. The proprioception CNN had over 99\% accuracy on our testing set (all six joint angles were within 1 degree of error) and had an average accumulative distance error of 0.77 mm during live testing, which is better than human finger proprioception. These proposed techniques offer soft robots the high-level ability to simultaneously perceive their proprioceptive state and peripheral environment, providing potential solutions for soft robots to solve everyday manipulation tasks. We believe the methods developed in this work can be widely applied to different designs and applications.Comment: Accepted to ICRA202