Shape Editing Research Articles

Generation and Editing of 2D Shapes Using a Branched Representation

In this article, we propose a new planar shape representation, the medial branch graph representation (MBGR) which allows to easily generate, vary and edit all-new sorts of parametric shapes. Each MBGR shape is described by a collection of connected control circles organized as a graph. Each pair of connected circles generates a branch of the shape. The boundaries of the branches are designed using cubic Bézier curves which enable shape representation in a compact SVG format. Each circle is associated with a regularity parameter locally controlling the smoothness of the branch connections. In this way, we can manage shapes having both sharp corners and smooth boundaries. To illustrate the potential of the MBGR representation, we have created an online facility ( https://mbgrg.github.io/mbgrg/ ) where the user can create/edit MBGR shapes automatically or interactively.

Deformation Recovery: Localized Learning for Detail-Preserving Deformations

We introduce a novel data-driven approach aimed at designing high-quality shape deformations based on a coarse localized input signal. Unlike previous data-driven methods that require a global shape encoding, we observe that detail-preserving deformations can be estimated reliably without any global context in certain scenarios. Building on this intuition, we leverage Jacobians defined in a one-ring neighborhood as a coarse representation of the deformation. Using this as the input to our neural network, we apply a series of MLPs combined with feature smoothing to learn the Jacobian corresponding to the detail-preserving deformation, from which the embedding is recovered by the standard Poisson solve. Crucially, by removing the dependence on a global encoding, every point becomes a training example, making the supervision particularly lightweight. Moreover, when trained on a class of shapes, our approach demonstrates remarkable generalization across different object categories. Equipped with this novel network, we explore three main tasks: refining an approximate shape correspondence, unsupervised deformation and mapping, and shape editing. Our code is made available at https://github.com/sentient07/LJN.

IShapEditing: Intelligent Shape Editing with Diffusion Models

AbstractRecent advancements in generative models have enabled image editing very effective with impressive results. By extending this progress to 3D geometry models, we introduce iShapEditing, a novel framework for 3D shape editing which is applicable to both generated and real shapes. Users manipulate shapes by dragging handle points to corresponding targets, offering an intuitive and intelligent editing interface. Leveraging the Triplane Diffusion model and robust intermediate feature correspondence, our framework utilizes classifier guidance to adjust noise representations during sampling process, ensuring alignment with user expectations while preserving plausibility. For real shapes, we employ shape predictions at each time step alongside a DDPM‐based inversion algorithm to derive their latent codes, facilitating seamless editing. iShapEditing provides effective and intelligent control over shapes without the need for additional model training or fine‐tuning. Experimental examples demonstrate the effectiveness and superiority of our method in terms of editing accuracy and plausibility.

Characteristic-Preserving Latent Space for Unpaired Cross-Domain Translation of 3D Point Clouds.

This article aims at unpaired shape-to-shape transformation for 3D point clouds, for instance, turning a chair to its table counterpart. Recent work for 3D shape transfer or deformation highly relies on paired inputs or specific correspondences. However, it is usually not feasible to assign precise correspondences or prepare paired data from two domains. A few methods start to study unpaired learning, but the characteristics of a source model may not be preserved after transformation. To overcome the difficulty of unpaired learning for transformation, we propose alternately training the autoencoder and translators to construct shape-aware latent space. This latent space based on novel loss functions enables our translators to transform 3D point clouds across domains and maintain the consistency of shape characteristics. We also crafted a test dataset to objectively evaluate the performance of point-cloud translation. The experiments demonstrate that our framework can construct high-quality models and retain more shape characteristics during cross-domain translation compared to the state-of-the-art methods. Moreover, we also present shape editing applications with our proposed latent space, including shape-style mixing and shape-type shifting, which do not require retraining a model.

Artistic style decomposition for texture and shape editing

AbstractWhile methods for generative image synthesis and example-based stylization produce impressive results, their black-box style representation intertwines shape, texture, and color aspects, limiting precise stylistic control and editing of artistic images. We introduce a novel method for decomposing the style of an artistic image that enables interactive geometric shape abstraction and texture control. We spatially decompose the input image into geometric shapes and an overlaying parametric texture representation, facilitating independent manipulation of color and texture. The parameters in this texture representation, comprising the image’s high-frequency details, control painterly attributes in a series of differentiable stylization filters. Shape decomposition is achieved using either segmentation or stroke-based neural rendering techniques. We demonstrate that our shape and texture decoupling enables diverse stylistic edits, including adjustments in shape, stroke, and painterly attributes such as contours and surface relief. Moreover, we demonstrate shape and texture style transfer in the parametric space using both reference images and text prompts and accelerate these by training networks for single- and arbitrary-style parameter prediction.

CharacterMixer: Rig‐Aware Interpolation of 3D Characters

AbstractWe present CharacterMixer, a system for blending two rigged 3D characters with different mesh and skeleton topologies while maintaining a rig throughout interpolation. CharacterMixer also enables interpolation during motion for such characters, a novel feature. Interpolation is an important shape editing operation, but prior methods have limitations when applied to rigged characters: they either ignore the rig (making interpolated characters no longer posable) or use a fixed rig and mesh topology. To handle different mesh topologies, CharacterMixer uses a signed distance field (SDF) representation of character shapes, with one SDF per bone. To handle different skeleton topologies, it computes a hierarchical correspondence between source and target character skeletons and interpolates the SDFs of corresponding bones. This correspondence also allows the creation of a single “unified skeleton” for posing and animating interpolated characters. We show that CharacterMixer produces qualitatively better interpolation results than two state‐of‐the‐art methods while preserving a rig throughout interpolation. Project page: https://seanxzhan.github.io/projects/CharacterMixer.

SENS: Part‐Aware Sketch‐based Implicit Neural Shape Modeling

AbstractWe present SENS, a novel method for generating and editing 3D models from hand‐drawn sketches, including those of abstract nature. Our method allows users to quickly and easily sketch a shape, and then maps the sketch into the latent space of a part‐aware neural implicit shape architecture. SENS analyzes the sketch and encodes its parts into ViT patch encoding, subsequently feeding them into a transformer decoder that converts them to shape embeddings suitable for editing 3D neural implicit shapes. SENS provides intuitive sketch‐based generation and editing, and also succeeds in capturing the intent of the user's sketch to generate a variety of novel and expressive 3D shapes, even from abstract and imprecise sketches. Additionally, SENS supports refinement via part reconstruction, allowing for nuanced adjustments and artifact removal. It also offers part‐based modeling capabilities, enabling the combination of features from multiple sketches to create more complex and customized 3D shapes. We demonstrate the effectiveness of our model compared to the state‐of‐the‐art using objective metric evaluation criteria and a user study, both indicating strong performance on sketches with a medium level of abstraction. Furthermore, we showcase our method's intuitive sketch‐based shape editing capabilities, and validate it through a usability study.

Dynamic borate ester bond-based 3D printing fluorescence polysiloxane with self-healing, antimicrobial, and shape memory

Developing a shape memory polysiloxanes possessing superior mechanical properties, antibacterial qualities, and exceptional self-healing capabilities has become a significant obstacle for the development of smart materials. We tackled this challenge by modifying polysiloxanes using N-acetylcysteine (NACL) and photosensitive molding with a dynamic borate ester crosslinker (S-BDB). The resultant photosensitive polysiloxane exhibited remarkable mechanical properties, with a stress up to 4.7 MPa. By utilizing the hydrogen bonds of grafted NACL and dynamic borate ester bonds of S-BDB to heal the damaged polymer network, the efficient self-healing performance (99.2 %) of photosensitive polysiloxane was obtained. Additionally, the material exhibited typical shape memory properties due to the rapid exchange of hydrogen bonds at elevated temperatures and the fixation of borate esters, enabling shape editing of the material. The material obtained a phosphorescent effect due to the introduction of B-O bonding, making it possible to have application value in the field of complex pattern fluorescent anti-counterfeiting marking manufacturing. Simultaneously, due to the amphiphilic ionic nature of NACL, the resin material obtained exhibited excellent antimicrobial properties.

Slippage-Preserving Reshaping of Human-Made 3D Content

Artists often need to reshape 3D models of human-made objects by changing the relative proportions or scales of different model parts or elements while preserving the look and structure of the inputs. Manually reshaping inputs to satisfy these criteria is highly time-consuming; the edit in our teaser took an artist 5 hours to complete. However, existing methods for 3D shape editing are largely designed for other tasks and produce undesirable outputs when repurposed for reshaping. Prior work on 2D curve network reshaping suggests that in 2D settings the user-expected outcome is achieved when the reshaping edit keeps the orientations of the different model elements and when these elements scale as-locally-uniformly-as-possible (ALUP). However, our observations suggest that in 3D viewers are tolerant of non-uniform tangential scaling if and when this scaling preserves slippage and reduces changes in element size, or scale , relative to the input. Slippage preservation requires surfaces which are locally slippable with respect to a given rigid motion to retain this property post-reshaping (a motion is slippable if when applied to the surface, it slides the surface along itself without gaps). We build on these observations by first extending the 2D ALUP framework to 3D and then modifying it to allow non-uniform scaling while promoting slippage and scale preservation. Our 3D ALUP extension produces reshaped outputs better aligned with viewer expectations than prior alternatives; our slippage-aware method further improves the outcome producing results on par with manual reshaping ones. Our method does not require any user input beyond specifying control handles and their target locations. We validate our method by applying it to over one hundred diverse inputs and by comparing our results to those generated by alternative approaches and manually. Comparative study participants preferred our outputs over the best performing traditional deformation method by a 65% margin and over our 3D ALUP extension by a 61% margin; they judged our outputs as at least on par with manually produced ones.

Interactive NeRF Geometry Editing With Shape Priors.

Neural Radiance Fields (NeRFs) have shown great potential for tasks like novel view synthesis of static 3D scenes. Since NeRFs are trained on a large number of input images, it is not trivial to change their content afterwards. Previous methods to modify NeRFs provide some control but they do not support direct shape deformation which is common for geometry representations like triangle meshes. In this paper, we present a NeRF geometry editing method that first extracts a triangle mesh representation of the geometry inside a NeRF. This mesh can be modified by any 3D modeling tool (we use ARAP mesh deformation). The mesh deformation is then extended into a volume deformation around the shape which establishes a mapping between ray queries to the deformed NeRF and the corresponding queries to the original NeRF. The basic shape editing mechanism is extended towards more powerful and more meaningful editing handles by generating box abstractions of the NeRF shapes which provide an intuitive interface to the user. By additionally assigning semantic labels, we can even identify and combine parts from different objects. We demonstrate the performance and quality of our method in a number of experiments on synthetic data as well as real captured scenes.

OptCtrlPoints: Finding the Optimal Control Points for Biharmonic 3D Shape Deformation

AbstractWe propose OptCtrlPoints, a data‐driven framework designed to identify the optimal sparse set of control points for reproducing target shapes using biharmonic 3D shape deformation. Control‐point‐based 3D deformation methods are widely utilized for interactive shape editing, and their usability is enhanced when the control points are sparse yet strategically distributed across the shape. With this objective in mind, we introduce a data‐driven approach that can determine the most suitable set of control points, assuming that we have a given set of possible shape variations. The challenges associated with this task primarily stem from the computationally demanding nature of the problem. Two main factors contribute to this complexity: solving a large linear system for the biharmonic weight computation and addressing the combinatorial problem of finding the optimal subset of mesh vertices. To overcome these challenges, we propose a reformulation of the biharmonic computation that reduces the matrix size, making it dependent on the number of control points rather than the number of vertices. Additionally, we present an efficient search algorithm that significantly reduces the time complexity while still delivering a nearly optimal solution. Experiments on SMPL, SMAL, and DeformingThings4D datasets demonstrate the efficacy of our method. Our control points achieve better template‐to‐target fit than FPS, random search, and neural‐network‐based prediction. We also highlight the significant reduction in computation time from days to approximately 3 minutes.

Catalyst-free, sustainable epoxy vitrimers from epoxidized soybean oil and natural sugar alcohols

Vitrimers based on dynamic transesterification (DTE) are usually thought to highly rely the use of catalysts, for example, Lewis acids and tertiary amines to promote the bond exchange reaction, which bring additional concerns on human health and the environment. Herein, we report a family of fully bio-based epoxy vitrimers that rearrange their topologies via DTE in the absence of any catalyst. Specifically, epoxidized soybean oil (ESO) was efficiently cured by conjugated carboxylic groups formed in-situ through the ring-opening of maleic anhydride (MA) by sugar alcohols (SAs) in a one-pot and solvent-free manner to produce these vitrimers. Abundant -OH groups from SAs strongly enhanced mechanical properties the without hindering stress relaxation or re-processing of the vitrimers. Meanwhile, welding, crack-healing and shape editing at high temperatures was achieved. The vitrimer samples exhibited various recovery rates of tensile strength upon 3 times of mechanical recycling.

Shape editing of kirigami-inspired thick-panel deployable structure

Thick-panel deployable structures have great engineering prospects in aerospace large-scale structures. However, achieving any desired shape of the deployable structure without seams, compact folding, and single-degree-of-freedom (single-DoF) deployment is a significant challenge for aerospace engineering applications. In this study, we propose an approach to edit the shape of kirigami-inspired thick-panel structures that enable single-DoF flat deploying and compact folding. First, based on two-vertex six-crease kirigami-inspired TPUs, modular assembly of single-DoF thick-panel deployable structures are achieved. Second, the single-DoF characteristics of resulting thick-panel structures are verified using screw theory and topology. Third, panel cutting and merging of the assembled thick-panel structures is performed to ensure the topological closed-loop characteristics in the desired shape of the structures while maintaining its single-DoF characteristics. In addition, their potential engineering applications in space solar arrays and exposing platforms are further explored. The proposed method provides some references for the design of future thick-panel deployable structures.

Interactive pose and shape editing with simple sketches from different viewing angles

The challenge of generating and creating humanoid 3D models is nowadays omnipresent in animation films, video games, and many other applications. To enhance and speed up this costly process, we propose an interactive method to infer 3D human poses and shapes from simple free-hand sketches. In contrast to previous work, our sketches are easy and fast to draw even for inexperienced users. Reconstructing the human 3D pose and shape from a single sketch is difficult due to the perspective projection resulting in ambiguous representations. We approach this challenge by adding a novel multi-view sketch interface for human pose estimation. Our simple sketch style allows for fast and intuitive, iterative refinement of not only the pose but also the shape using simple 2D interactions. The pose is inferred from a pre-trained neural network that is trained on a self-generated data set of sketches, derived by sampling 3D STAR models and rendering them in a sketch style. We conduct a user study to evaluate the accuracy, intuitiveness, and usability of our system in a real-world scenario. With our novel multi-view input system, we can effectively resolve ambiguities inherent to single sketch approaches while still delivering a fast and intuitive user experience.

Isotropic ARAP Energy Using Cauchy-Green Invariants

Isotropic As-Rigid-As-Possible (ARAP) energy has been popular for shape editing, mesh parametrisation and soft-body simulation for almost two decades. However, a formulation using Cauchy-Green (CG) invariants has always been unclear, due to a rotation-polluted trace term that cannot be directly expressed using these invariants. We show how this incongruent trace term can be understood via an implicit relationship to the CG invariants. Our analysis reveals this relationship to be a polynomial where the roots equate to the trace term, and where the derivatives also give rise to closed-form expressions of the Hessian to guarantee positive semi-definiteness for a fast and concise Newton-type implicit time integration. A consequence of this analysis is a novel analytical formulation to compute rotations and singular values of deformation-gradient tensors without explicit/numerical factorization which is significant, resulting in up-to 3.5× speedup and benefits energy function evaluation for reducing solver time. We validate our energy formulation by experiments and comparison, demonstrating that our resulting eigendecomposition using the CG invariants is equivalent to existing ARAP formulations. We thus reveal isotropic ARAP energy to be a member of the "Cauchy-Green club", meaning that it can indeed be defined using CG invariants and therefore that the closed-form expressions of the resulting Hessian are shared with other energies written in their terms.

Generative Deformable Radiance Fields for Disentangled Image Synthesis of Topology‐Varying Objects

Abstract3D‐aware generative models have demonstrated their superb performance to generate 3D neural radiance fields (NeRF) from a collection of monocular 2D images even for topology‐varying object categories. However, these methods still lack the capability to separately control the shape and appearance of the objects in the generated radiance fields. In this paper, we propose a generative model for synthesizing radiance fields of topology‐varying objects with disentangled shape and appearance variations. Our method generates deformable radiance fields, which builds the dense correspondence between the density fields of the objects and encodes their appearances in a shared template field. Our disentanglement is achieved in an unsupervised manner without introducing extra labels to previous 3D‐aware GAN training. We also develop an effective image inversion scheme for reconstructing the radiance field of an object in a real monocular image and manipulating its shape and appearance. Experiments show that our method can successfully learn the generative model from unstructured monocular images and well disentangle the shape and appearance for objects (e.g., chairs) with large topological variance. The model trained on synthetic data can faithfully reconstruct the real object in a given single image and achieve high‐quality texture and shape editing results.

EditVAE: Unsupervised Parts-Aware Controllable 3D Point Cloud Shape Generation

This paper tackles the problem of parts-aware point cloud generation. Unlike existing works which require the point cloud to be segmented into parts a priori, our parts-aware editing and generation are performed in an unsupervised manner. We achieve this with a simple modification of the Variational Auto-Encoder which yields a joint model of the point cloud itself along with a schematic representation of it as a combination of shape primitives. In particular, we introduce a latent representation of the point cloud which can be decomposed into a disentangled representation for each part of the shape. These parts are in turn disentangled into both a shape primitive and a point cloud representation, along with a standardising transformation to a canonical coordinate system. The dependencies between our standardising transformations preserve the spatial dependencies between the parts in a manner that allows meaningful parts-aware point cloud generation and shape editing. In addition to the flexibility afforded by our disentangled representation, the inductive bias introduced by our joint modeling approach yields state-of-the-art experimental results on the ShapeNet dataset.

Illuminating the path to DNA repair

Diverse DNA repair pathways correct ranging types of damage and play central roles in genome editing. In this issue of Cell, two publications leverage a new high-throughput screen that links pathway genes with the outcomes of repair, yielding mechanistic insights into the repair process as well as means to shape editing outcomes.

ShapeMOD

A popular way to create detailed yet easily controllable 3D shapes is via procedural modeling, i.e. generating geometry using programs. Such programs consist of a series of instructions along with their associated parameter values. To fully realize the benefits of this representation, a shape program should be compact and only expose degrees of freedom that allow for meaningful manipulation of output geometry. One way to achieve this goal is to design higher-level macro operators that, when executed, expand into a series of commands from the base shape modeling language. However, manually authoring such macros, much like shape programs themselves, is difficult and largely restricted to domain experts. In this paper, we present ShapeMOD, an algorithm for automatically discovering macros that are useful across large datasets of 3D shape programs. ShapeMOD operates on shape programs expressed in an imperative, statement-based language. It is designed to discover macros that make programs more compact by minimizing the number of function calls and free parameters required to represent an input shape collection. We run ShapeMOD on multiple collections of programs expressed in a domain-specific language for 3D shape structures. We show that it automatically discovers a concise set of macros that abstract out common structural and parametric patterns that generalize over large shape collections. We also demonstrate that the macros found by ShapeMOD improve performance on downstream tasks including shape generative modeling and inferring programs from point clouds. Finally, we conduct a user study that indicates that ShapeMOD's discovered macros make interactive shape editing more efficient.

Variational Autoencoders for Localized Mesh Deformation Component Analysis

Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale nonlinear deformations, and may not always be able to identify important deformation components. In this paper we propose a mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations, assuming that the analyzed dataset contains meshes with the same vertex connectivity, which is common for deformation analysis. To help localize deformations, we introduce sparse regularization in this framework, along with spectral graph convolutional operations. Through modifying the regularization formulation and allowing dynamic change of sparsity ranges, we improve the visual quality and reconstruction ability of the extracted deformation components. Our system also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. As an important application of localized deformation components and a novel approach on its own, we further develop a neural shape editing method, achieving shape editing and deformation component extraction in a unified framework, and ensuring plausibility of the edited shapes. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. We also demonstrate the effectiveness of our method for neural shape editing.