14 accepted papers
We introduce a framework for converting 3D shapes into compact and editable assemblies of analytic primitives, directly addressing the persistent trade-off between reconstruction fidelity and parsimony. Our approach combines two key contributions: a novel primitive, termed SuperFrustum, and an itera
We introduce WorldGen, a method for generating large, fully formed, navigable 3D worlds from a single text prompt. Existing approaches to 3D scene generation often trade off scene diversity, completeness, and correctness in different ways. We push this envelope by producing large scenes explicitly d
We propose a generative technique to edit 3D shapes, represented as meshes, NeRFs, or Gaussian Splats, in ~3 seconds, without the need for running an SDS type of optimization.Our key insight is to cast 3D editing as a multiview image inpainting problem, as this representation is generic and can be m…
Pattern images are everywhere in the digital and physical worlds, and tools to edit them are valuable. But editing pattern images is tricky: desired edits are often *programmatic*: structure-aware edits that alter the underlying program which generates the pattern. One could attempt to infer this un…
Text-to-image diffusion models enable high-quality image generation but are computationally expensive, especially when producing large image collections. While prior work optimizes per-inference efficiency, we explore an orthogonal approach: reducing redundancy across multiple correlated prompts. Ou…
We present GigaPose a fast robust and accurate method for CAD-based novel object pose estimation in RGB images. GigaPose first leverages discriminative "templates" rendered images of the CAD models to recover the out-of-plane rotation and then uses patch correspondences to estimate the four remainin…
Current controls over diffusion models (e.g. through text or ControlNet) for image generation fall short in recognizing abstract continuous attributes like illumination direction or non-rigid shape change. In this paper we present an approach for allowing users of text-to-image models to have fine-g…
The practicality of 3D object pose estimation remains limited for many applications due to the need for prior knowledge of a 3D model and a training period for new objects. To address this limitation we propose an approach that takes a single image of a new object as input and predicts the relative…
This work proposes a novel representation of injective deformations of 3D space which overcomes existing limitations of injective methods namely inaccuracy lack of robustness and incompatibility with general learning and optimization frameworks. Our core idea is to reduce the problem to a "deep" com…
We present 3DMiner -- a pipeline for mining 3D shapes from challenging large-scale unannotated image datasets. Unlike other unsupervised 3D reconstruction methods, we assume that, within a large-enough dataset, there must exist images of objects with similar shapes but varying backgrounds, textures,…
Recent advances in image clustering typically focus on learning better deep representations. In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict transformations and performs clustering directly in image space. This learning process nat…
We propose to represent shapes as the deformation and combination of learnt elementary 3D structures. We demonstrate this decomposition in learnt elementary 3D structures is highly interpretable and leads to clear improvements in 3D shape generation and matching. More precisely, we present two comp…
We present a new deep learning approach for matching deformable shapes by introducing Shape Deformation Networks which jointly encode 3D shapes and correspondences. This is achieved by factoring the surface representation into (i) a template, that parameterizes the surface, and (ii) a learnt global…
We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, o…