RAID: A relation-augmented image descriptor

As humans, we regularly interpret scenes based on how objects are related, rather than based on the objects themselves. For example, we see a person riding an object X or a plank bridging two objects. Current methods provide limited support to search for content based on such relations. We present RAID, a relation-augmented image descriptor that supports queries based on inter-region relations. The key idea of our descriptor is to encode region-to-region relations as the spatial distribution of point-to-region relationships between two image regions. RAID allows sketch-based retrieval and requires minimal training data, thus making it suited even for querying uncommon relations. We evaluate the proposed descriptor by querying into large image databases and successfully extract nontrivial images demonstrating complex inter-region relations, which are easily missed or erroneously classified by existing methods. We assess the robustness of RAID on multiple datasets even when the region segmentation is computed automatically or very noisy

StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows

High-quality, diverse, and photorealistic images can now be generated by unconditional GANs (e.g., StyleGAN). However, limited options exist to control the generation process using (semantic) attributes while still preserving the quality of the output. Further, due to the entangled nature of the GAN latent space, performing edits along one attribute can easily result in unwanted changes along other attributes. In this article, in the context of conditional exploration of entangled latent spaces, we investigate the two sub-problems of attribute-conditioned sampling and attribute-controlled editing. We present StyleFlow as a simple, effective, and robust solution to both the sub-problems by formulating conditional exploration as an instance of conditional continuous normalizing flows in the GAN latent space conditioned by attribute features. We evaluate our method using the face and the car latent space of StyleGAN, and demonstrate fine-grained disentangled edits along various attributes on both real photographs and StyleGAN generated images. For example, for faces, we vary camera pose, illumination variation, expression, facial hair, gender, and age. Finally, via extensive qualitative and quantitative comparisons, we demonstrate the superiority of StyleFlow over prior and several concurrent works. Project Page and Video: https://rameenabdal.github.io/StyleFlow

Computational Design of Lightweight Trusses

Trusses are load-carrying light-weight structures consisting of bars connected at joints ubiquitously applied in a variety of engineering scenarios. Designing optimal trusses that satisfy functional specifications with a minimal amount of material has interested both theoreticians and practitioners for more than a century. In this paper, we introduce two main ideas to improve upon the state of the art. First, we formulate an alternating linear programming problem for geometry optimization. Second, we introduce two sets of complementary topological operations, including a novel subdivision scheme for global topology refinement inspired by Michell's famed theoretical study. Based on these two ideas, we build an efficient computational framework for the design of lightweight trusses. \AD{We illustrate our framework with a variety of functional specifications and extensions. We show that our method achieves trusses with smaller volumes and is over two orders of magnitude faster compared with recent state-of-the-art approaches

InsetGAN for Full-Body Image Generation

While GANs can produce photo-realistic images in ideal conditions for certain domains, the generation of full-body human images remains difficult due to the diversity of identities, hairstyles, clothing, and the variance in pose. In-stead of modeling this complex domain with a single GAN, we propose a novel method to combine multiple pretrained GANs, where one GAN generates a global canvas (e.g., human body) and a set of specialized GANs, or insets, focus on different parts (e.g., faces, shoes) that can be seamlessly inserted onto the global canvas. We model the problem as jointly exploring the respective latent spaces such that the generated images can be combined, by inserting the parts from the specialized generators onto the global canvas, without introducing seams. We demonstrate the setup by combining a full body GAN with a dedicated high-quality face GAN to produce plausible-looking humans. We evalu-ate our results with quantitative metrics and user studies

SketchGen: Generating Constrained CAD Sketches

Computer-aided design (CAD) is the most widely used modeling approach for technical design. The typical starting point in these designs is 2D sketches which can later be extruded and combined to obtain complex three-dimensional assemblies. Such sketches are typically composed of parametric primitives, such as points, lines, and circular arcs, augmented with geometric constraints linking the primitives, such as coincidence, parallelism, or orthogonality. Sketches can be represented as graphs, with the primitives as nodes and the constraints as edges. Training a model to automatically generate CAD sketches can enable several novel workflows, but is challenging due to the complexity of the graphs and the heterogeneity of the primitives and constraints. In particular, each type of primitive and constraint may require a record of different size and parameter types. We propose SketchGen as a generative model based on a transformer architecture to address the heterogeneity problem by carefully designing a sequential language for the primitives and constraints that allows distinguishing between different primitive or constraint types and their parameters, while encouraging our model to re-use information across related parameters, encoding shared structure. A particular highlight of our work is the ability to produce primitives linked via constraints that enables the final output to be further regularized via a constraint solver. We evaluate our model by demonstrating constraint prediction for given sets of primitives and full sketch generation from scratch, showing that our approach significantly out performs the state-of-the-art in CAD sketch generation

How Do Users Map Points Between Dissimilar Shapes

Finding similar points in globally or locally similar shapes has been studied extensively through the use of various point descriptors or shape-matching methods. However, little work exists on finding similar points in dissimilar shapes. In this paper, we present the results of a study where users were given two dissimilar two-dimensional shapes and asked to map a given point in the first shape to the point in the second shape they consider most similar. We find that user mappings in this study correlate strongly with simple geometric relationships between points and shapes. To predict the probability distribution of user mappings between any pair of simple two-dimensional shapes, two distinct statistical models are defined using these relationships. We perform a thorough validation of the accuracy of these predictions and compare our models qualitatively and quantitatively to well-known shape-matching methods. Using our predictive models, we propose an approach to map objects or procedural content between different shapes in different design scenarios

Computational network design from functional specifications

Connectivity and layout of underlying networks largely determine agent behavior and usage in many environments. For example, transportation networks determine the flow of traffic in a neighborhood, whereas building floorplans determine the flow of people in a workspace. Designing such networks from scratch is challenging as even local network changes can have large global effects. We investigate how to computationally create networks starting from only high-level functional specifications. Such specifications can be in the form of network density, travel time versus network length, traffic type, destination location, etc. We propose an integer programming-based approach that guarantees that the resultant networks are valid by fulfilling all the specified hard constraints and that they score favorably in terms of the objective function. We evaluate our algorithm in two different design settings, street layout and floorplans to demonstrate that diverse networks can emerge purely from high-level functional specifications

FrankenGAN: guided detail synthesis for building mass models using style-synchonized GANs

Coarse building mass models are now routinely generated at scales ranging from individual buildings to whole cities. Such models can be abstracted from raw measurements, generated procedurally, or created manually. However, these models typically lack any meaningful geometric or texture details, making them unsuitable for direct display. We introduce the problem of automatically and realistically decorating such models by adding semantically consistent geometric details and textures. Building on the recent success of generative adversarial networks (GANs), we propose FrankenGAN, a cascade of GANs that creates plausible details across multiple scales over large neighborhoods. The various GANs are synchronized to produce consistent style distributions over buildings and neighborhoods. We provide the user with direct control over the variability of the output. We allow him/her to interactively specify the style via images and manipulate style-adapted sliders to control style variability. We test our system on several large-scale examples. The generated outputs are qualitatively evaluated via a set of perceptual studies and are found to be realistic, semantically plausible, and consistent in style

Interactive architectural modeling with procedural extrusions

We present an interactive procedural modeling system for the exterior of architectural models. Our modeling system is based on procedural extrusions of building footprints. The main novelty of our work is that we can model difficult architectural surfaces in a procedural framework, e.g. curved roofs, overhanging roofs, dormer windows, interior dormer windows, roof constructions with vertical walls, buttresses, chimneys, bay windows, columns, pilasters, and alcoves. We present a user interface to interactively specify procedural extrusions, a sweep plane algorithm to compute a two-manifold architectural surface, and applications to architectural modeling