Data-driven shape interpolation and morphing editing

Shape interpolation has many applications in computer graphics such as morphing for computer animation. In this paper, we propose a novel data-driven mesh interpolation method. We adapt patch-based linear rotational invariant coordinates to effectively represent deformations of models in a shape collection, and utilize this information to guide the synthesis of interpolated shapes. Unlike previous data-driven approaches, we use a rotation/translation invariant representation which defines the plausible deformations in a global continuous space. By effectively exploiting the knowledge in the shape space, our method produces realistic interpolation results at interactive rates, outperforming state-of-the-art methods for challenging cases. We further propose a novel approach to interactive editing of shape morphing according to the shape distribution. The user can explore the morphing path and select example models intuitively and adjust the path with simple interactions to edit the morphing sequences. This provides a useful tool to allow users to generate desired morphing with little effort. We demonstrate the effectiveness of our approach using various examples

A Revisit of Shape Editing Techniques: from the Geometric to the Neural Viewpoint

3D shape editing is widely used in a range of applications such as movie production, computer games and computer aided design. It is also a popular research topic in computer graphics and computer vision. In past decades, researchers have developed a series of editing methods to make the editing process faster, more robust, and more reliable. Traditionally, the deformed shape is determined by the optimal transformation and weights for an energy term. With increasing availability of 3D shapes on the Internet, data-driven methods were proposed to improve the editing results. More recently as the deep neural networks became popular, many deep learning based editing methods have been developed in this field, which is naturally data-driven. We mainly survey recent research works from the geometric viewpoint to those emerging neural deformation techniques and categorize them into organic shape editing methods and man-made model editing methods. Both traditional methods and recent neural network based methods are reviewed

Efficient and flexible deformation representation for data-driven surface modeling

Effectively characterizing the behavior of deformable objects has wide applicability but remains challenging. We present a new rotation-invariant deformation representation and a novel reconstruction algorithm to accurately reconstruct the positions and local rotations simultaneously. Meshes can be very efficiently reconstructed from our representation by matrix pre-decomposition, while, at the same time, hard or soft constraints can be flexibly specified with only positions of handles needed. Our approach is thus particularly suitable for constrained deformations guided by examples, providing significant benefits over state-of-the-art methods. Based on this, we further propose novel data-driven approaches to mesh deformation and non-rigid registration of deformable objects. Both problems are formulated consistently as finding an optimized model in the shape space that satisfies boundary constraints, either specified by the user, or according to the scan. By effectively exploiting the knowledge in the shape space, our method produces realistic deformation results in real-time and produces high quality registrations from a template model to a single noisy scan captured using a low-quality depth camera, outperforming state-of-the-art methods

Data-driven shape analysis and processing

Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing

Efficient sketch-based 3D character modelling.

Sketch-based modelling (SBM) has undergone substantial research over the past two decades. In the early days, researchers aimed at developing techniques useful for modelling of architectural and mechanical models through sketching. With the advancement of technology used in designing visual effects for film, TV and games, the demand for highly realistic 3D character models has skyrocketed. To allow artists to create 3D character models quickly, researchers have proposed several techniques for efficient character modelling from sketched feature curves. Moreover several research groups have developed 3D shape databases to retrieve 3D models from sketched inputs. Unfortunately, the current state of the art in sketch-based organic modelling (3D character modelling) contains a lot of gaps and limitations. To bridge the gaps and improve the current sketch-based modelling techniques, this research aims to develop an approach allowing direct and interactive modelling of 3D characters from sketched feature curves, and also make use of 3D shape databases to guide the artist to create his / her desired models. The research involved finding a fusion between 3D shape retrieval, shape manipulation, and shape reconstruction / generation techniques backed by an extensive literature review, experimentation and results. The outcome of this research involved devising a novel and improved technique for sketch-based modelling, the creation of a software interface that allows the artist to quickly and easily create realistic 3D character models with comparatively less effort and learning. The proposed research work provides the tools to draw 3D shape primitives and manipulate them using simple gestures which leads to a better modelling experience than the existing state of the art SBM systems

A Data-Driven Approach to Realistic Shape Morphing

Morphing between 3D objects is a fundamental technique in computer graphics. Traditional methods of shape morphing focus on establishing meaningful correspondences and finding smooth interpolation between shapes. Such methods however only take geometric information as input and thus cannot in general avoid producing unnatural interpolation, in particular for large-scale deformations. This paper proposes a novel data-driven approach for shape morphing. Given a database with various models belonging to the same category, we treat them as data samples in the plausible deformation space. These models are then clustered to form local shape spaces of plausible deformations. We use a simple metric to reasonably represent the closeness between pairs of models. Given source and target models, the morphing problem is casted as a global optimization problem of finding a minimal distance path within the local shape spaces connecting these models. Under the guidance of intermediate models in the path, an extended as-rigid-as-possible interpolation is used to produce the final morphing. By exploiting the knowledge of plausible models, our approach produces realistic morphing for challenging cases as demonstrated by various examples in the paper