Modeling 3D animals from a side-view sketch

Shape Modeling International 2014International audienceUsing 2D contour sketches as input is an attractive solution for easing the creation of 3D models. This paper tackles the problem of creating 3D models of animals from a single, side-view sketch. We use the a priori assumptions of smoothness and structural symmetry of the animal about the sagittal plane to inform the 3D reconstruction. Our contributions include methods for identifying and inferring the contours of shape parts from the input sketch, a method for identifying the hierarchy of these structural parts including the detection of approximate symmetric pairs, and a hierarchical algorithm for positioning and blending these parts into a consistent 3D implicit-surface-based model. We validate this pipeline by showing that a number of plausible animal shapes can be automatically constructed from a single sketch

Sketch-based modeling with a differentiable renderer

© 2020 The Authors. Computer Animation and Virtual Worlds published by John Wiley & Sons, Ltd. Sketch-based modeling aims to recover three-dimensional (3D) shape from two-dimensional line drawings. However, due to the sparsity and ambiguity of the sketch, it is extremely challenging for computers to interpret line drawings of physical objects. Most conventional systems are restricted to specific scenarios such as recovering for specific shapes, which are not conducive to generalize. Recent progress of deep learning methods have sparked new ideas for solving computer vision and pattern recognition issues. In this work, we present an end-to-end learning framework to predict 3D shape from line drawings. Our approach is based on a two-steps strategy, it converts the sketch image to its normal image, then recover the 3D shape subsequently. A differentiable renderer is proposed and incorporated into this framework, it allows the integration of the rendering pipeline with neural networks. Experimental results show our method outperforms the state-of-art, which demonstrates that our framework is able to cope with the challenges in single sketch-based 3D shape modeling

Modeling Symmetric Developable Surfaces from a Single Photo

National audienceWe propose a method to reconstruct 3D developable surfaces from a single 2D drawing traced and annotatedover a side-view photo of a partially symmetrical object. Our reconstruction algorithm combines symmetry andorthogonality shapes cues within a unified optimization framework that solves for the 3D position of the Beziercontrol points of the drawn curves while being tolerant to drawing inaccuracy and perspective distortions. We thenrely on existing surface optimization methods to produce a developable surface that interpolates our 3D curves.Our method is particularly well suited for the modeling and fabrication of fashion items as it converts the inputdrawing into flattened developable patterns ready for sewing

Inferring Implicit 3D Representations from Human Figures on Pictorial Maps

In this work, we present an automated workflow to bring human figures, one of the most frequently appearing entities on pictorial maps, to the third dimension. Our workflow is based on training data and neural networks for single-view 3D reconstruction of real humans from photos. We first let a network consisting of fully connected layers estimate the depth coordinate of 2D pose points. The gained 3D pose points are inputted together with 2D masks of body parts into a deep implicit surface network to infer 3D signed distance fields (SDFs). By assembling all body parts, we derive 2D depth images and body part masks of the whole figure for different views, which are fed into a fully convolutional network to predict UV images. These UV images and the texture for the given perspective are inserted into a generative network to inpaint the textures for the other views. The textures are enhanced by a cartoonization network and facial details are resynthesized by an autoencoder. Finally, the generated textures are assigned to the inferred body parts in a ray marcher. We test our workflow with 12 pictorial human figures after having validated several network configurations. The created 3D models look generally promising, especially when considering the challenges of silhouette-based 3D recovery and real-time rendering of the implicit SDFs. Further improvement is needed to reduce gaps between the body parts and to add pictorial details to the textures. Overall, the constructed figures may be used for animation and storytelling in digital 3D maps.Comment: to be published in 'Cartography and Geographic Information Science

Lifting Freehand Concept Sketches into 3D

International audienceWe present the first algorithm capable of automatically lifting real-world, vector-format, industrial design sketches into 3D. Targeting real-world sketches raises numerous challenges due to inaccuracies, use of overdrawn strokes, and construction lines. In particular, while construction lines convey important 3D information, they add significant clutter and introduce multiple accidental 2D intersections. Our algorithm exploits the geometric cues provided by the construction lines and lifts them to 3D by computing their intended 3D intersections and depths. Once lifted to 3D, these lines provide valuable geometric constraints that we leverage to infer the 3D shape of other artist drawn strokes. The core challenge we address is inferring the 3D connectivity of construction and other lines from their 2D projections by separating 2D intersections into 3D intersections and accidental occlusions. We efficiently address this complex combinatorial problem using a dedicated search algorithm that leverages observations about designer drawing preferences , and uses those to explore only the most likely solutions of the 3D intersection detection problem. We demonstrate that our separator outputs are of comparable quality to human annotations, and that the 3D structures we recover enable a range of design editing and visualization applications, including novel view synthesis and 3D-aware scaling of the depicted shape

On expert performance in 3D curve-drawing tasks

A study is described which examines the drawing accuracy of experts when drawing foreshortened projections of 3D curves in ecologically-valid conditions. The main result of this study is that the distribution of error in expert drawings exhibits a bias similar to that previously observed in non-expert subjects, which is dependent on the degree of foreshortening of the imagined drawing surface. A review of existing perceptual studies also finds that only absolute 2D image-space error has been considered, which has been found to be largest with viewing angles of 25-55 ◦. Our visualizations of 3D error indicate that 3D bias continues to increase with decreasing viewing angle. Based on these findings, we analyze current 3D curve drawing techniques for susceptibility to foreshortening bias, and make suggestions for future sketch-based modeling systems

True2Form: 3D Curve Networks from 2D Sketches via Selective Regularization

International audienceTrue2Form is a sketch-based modeling system that reconstructs 3D curves from typical design sketches. Our approach to infer 3D form from 2D drawings is a novel mathematical framework of insights derived from perception and design literature. We note that designers favor viewpoints that maximally reveal 3D shape information, and strategically sketch descriptive curves that convey intrinsic shape properties, such as curvature, symmetry, or parallelism. Studies indicate that viewers apply these properties selectively to envision a globally consistent 3D shape. We mimic this selective regularization algorithmically, by progressively detecting and enforcing applicable properties, accounting for their global impact on an evolving 3D curve network. Balancing regularity enforcement against sketch fidelity at each step allows us to correct for inaccuracy inherent in free-hand sketching. We perceptually validate our approach by showing agreement between our algorithm and viewers in selecting applicable regularities. We further evaluate our solution by: reconstructing a range of 3D models from diversely sourced sketches; comparisons to prior art; and visual comparison to both ground-truth and 3D reconstructions by designers

GarmentCode: Programming Parametric Sewing Patterns

Garment modeling is an essential task of the global apparel industry and a core part of digital human modeling. Realistic representation of garments with valid sewing patterns is key to their accurate digital simulation and eventual fabrication. However, little-to-no computational tools provide support for bridging the gap between high-level construction goals and low-level editing of pattern geometry, e.g., combining or switching garment elements, semantic editing, or design exploration that maintains the validity of a sewing pattern. We suggest the first DSL for garment modeling -- GarmentCode -- that applies principles of object-oriented programming to garment construction and allows designing sewing patterns in a hierarchical, component-oriented manner. The programming-based paradigm naturally provides unique advantages of component abstraction, algorithmic manipulation, and free-form design parametrization. We additionally support the construction process by automating typical low-level tasks like placing a dart at a desired location. In our prototype garment configurator, users can manipulate meaningful design parameters and body measurements, while the construction of pattern geometry is handled by garment programs implemented with GarmentCode. Our configurator enables the free exploration of rich design spaces and the creation of garments using interchangeable, parameterized components. We showcase our approach by producing a variety of garment designs and retargeting them to different body shapes using our configurator.Comment: Supplementary video: https://youtu.be/16Yyr2G9_6E