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Found 24 papers, 7 papers with code
Text-guided Controllable Mesh Refinement for Interactive 3D Modeling
First, we generate a single-view RGB image conditioned on the input coarse geometry and the input text prompt.
DMesh: A Differentiable Mesh Representation
We present a differentiable representation, DMesh, for general 3D triangular meshes.
GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis
We introduce GEM3D -- a new deep, topology-aware generative model of 3D shapes.
Learning Continuous 3D Words for Text-to-Image Generation
We do this by engineering special sets of input tokens that can be transformed in a continuous manner -- we call them Continuous 3D Words.
Diffusion Handles Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D
Diffusion handles is a novel approach to enable 3D object edits on diffusion images requiring only existing pre-trained diffusion models depth estimation without any fine-tuning or 3D object retrieval.
Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models
Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path.
Diffusion Handles: Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D
Our key insight is to lift diffusion activations for an object to 3D using a proxy depth, 3D-transform the depth and associated activations, and project them back to image space.
3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets
We present 3DMiner -- a pipeline for mining 3D shapes from challenging large-scale unannotated image datasets.
Accidental Turntables: Learning 3D Pose by Watching Objects Turn
We propose a technique for learning single-view 3D object pose estimation models by utilizing a new source of data -- in-the-wild videos where objects turn.
Recovering Detail in 3D Shapes Using Disparity Maps
We present a fine-tuning method to improve the appearance of 3D geometries reconstructed from single images.
PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos
We present PlanarRecon -- a novel framework for globally coherent detection and reconstruction of 3D planes from a posed monocular video.
ANISE: Assembly-based Neural Implicit Surface rEconstruction
Reconstructions can be obtained in two ways: (i) by directly decoding the part latent codes to part implicit functions, then combining them into the final shape; or (ii) by using part latents to retrieve similar part instances in a part database and assembling them in a single shape.
PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation
We present PriFit, a semi-supervised approach for label-efficient learning of 3D point cloud segmentation networks.
Deep Manifold Prior
We present a prior for manifold structured data, such as surfaces of 3D shapes, where deep neural networks are adopted to reconstruct a target shape using gradient descent starting from a random initialization.
Learning Generative Models of Shape Handles
We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, shape parsing, and building compact 3D representations.
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years.
Shape Reconstruction Using Differentiable Projections and Deep Priors
We investigate the problem of reconstructing shapes from noisy and incomplete projections in the presence of viewpoint uncertainities.
Inferring 3D Shapes from Image Collections using Adversarial Networks
To this end, we present new differentiable projection operators that can be used by PrGAN to learn better 3D generative models.
A Bayesian Perspective on the Deep Image Prior
The deep image prior was recently introduced as a prior for natural images.
A Deeper Look at 3D Shape Classifiers
We investigate the role of representations and architectures for classifying 3D shapes in terms of their computational efficiency, generalization, and robustness to adversarial transformations.
Multiresolution Tree Networks for 3D Point Cloud Processing
We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks.
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints.
Shape Generation using Spatially Partitioned Point Clouds
We propose to use the expressive power of neural networks to learn a distribution over the shape coefficients in a generative-adversarial framework.
3D Shape Induction from 2D Views of Multiple Objects
In this paper we investigate the problem of inducing a distribution over three-dimensional structures given two-dimensional views of multiple objects taken from unknown viewpoints.
