3Doodle: Compact Abstraction of Objects with 3D Strokes
- URL: http://arxiv.org/abs/2402.03690v2
- Date: Mon, 29 Apr 2024 11:03:13 GMT
- Title: 3Doodle: Compact Abstraction of Objects with 3D Strokes
- Authors: Changwoon Choi, Jaeah Lee, Jaesik Park, Young Min Kim,
- Abstract summary: We propose 3Dooole, generating descriptive and view-consistent sketch images.
Our method is based on the idea that a set of 3D strokes can efficiently represent 3D structural information.
The resulting sparse set of 3D strokes can be rendered as abstract sketches containing essential 3D characteristic shapes of various objects.
- Score: 30.87733869892925
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While free-hand sketching has long served as an efficient representation to convey characteristics of an object, they are often subjective, deviating significantly from realistic representations. Moreover, sketches are not consistent for arbitrary viewpoints, making it hard to catch 3D shapes. We propose 3Dooole, generating descriptive and view-consistent sketch images given multi-view images of the target object. Our method is based on the idea that a set of 3D strokes can efficiently represent 3D structural information and render view-consistent 2D sketches. We express 2D sketches as a union of view-independent and view-dependent components. 3D cubic B ezier curves indicate view-independent 3D feature lines, while contours of superquadrics express a smooth outline of the volume of varying viewpoints. Our pipeline directly optimizes the parameters of 3D stroke primitives to minimize perceptual losses in a fully differentiable manner. The resulting sparse set of 3D strokes can be rendered as abstract sketches containing essential 3D characteristic shapes of various objects. We demonstrate that 3Doodle can faithfully express concepts of the original images compared with recent sketch generation approaches.
Related papers
- Diff3DS: Generating View-Consistent 3D Sketch via Differentiable Curve Rendering [17.918603435615335]
3D sketches are widely used for visually representing the 3D shape and structure of objects or scenes.
We propose Diff3DS, a novel differentiable framework for generating view-consistent 3D sketch.
Our framework bridges the domains of 3D sketch and customized image, achieving end-toend optimization of 3D sketch.
arXiv Detail & Related papers (2024-05-24T07:48:14Z) - Sketch3D: Style-Consistent Guidance for Sketch-to-3D Generation [55.73399465968594]
This paper proposes a novel generation paradigm Sketch3D to generate realistic 3D assets with shape aligned with the input sketch and color matching the textual description.
Three strategies are designed to optimize 3D Gaussians, i.e., structural optimization via a distribution transfer mechanism, color optimization with a straightforward MSE loss and sketch similarity optimization with a CLIP-based geometric similarity loss.
arXiv Detail & Related papers (2024-04-02T11:03:24Z) - Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes [118.406721663244]
We introduce a novel part-level modelling and alignment framework that facilitates abstraction modelling and cross-modal correspondence.
Our approach seamlessly extends to sketch modelling by establishing correspondence between CLIPasso edgemaps and projected 3D part regions.
arXiv Detail & Related papers (2023-12-07T05:04:33Z) - Control3D: Towards Controllable Text-to-3D Generation [107.81136630589263]
We present a text-to-3D generation conditioning on the additional hand-drawn sketch, namely Control3D.
A 2D conditioned diffusion model (ControlNet) is remoulded to guide the learning of 3D scene parameterized as NeRF.
We exploit a pre-trained differentiable photo-to-sketch model to directly estimate the sketch of the rendered image over synthetic 3D scene.
arXiv Detail & Related papers (2023-11-09T15:50:32Z) - ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image
Collections [71.46546520120162]
Estimating 3D articulated shapes like animal bodies from monocular images is inherently challenging.
We propose ARTIC3D, a self-supervised framework to reconstruct per-instance 3D shapes from a sparse image collection in-the-wild.
We produce realistic animations by fine-tuning the rendered shape and texture under rigid part transformations.
arXiv Detail & Related papers (2023-06-07T17:47:50Z) - Make Your Brief Stroke Real and Stereoscopic: 3D-Aware Simplified Sketch
to Portrait Generation [51.64832538714455]
Existing studies only generate portraits in the 2D plane with fixed views, making the results less vivid.
In this paper, we present Stereoscopic Simplified Sketch-to-Portrait (SSSP), which explores the possibility of creating Stereoscopic 3D-aware portraits.
Our key insight is to design sketch-aware constraints that can fully exploit the prior knowledge of a tri-plane-based 3D-aware generative model.
arXiv Detail & Related papers (2023-02-14T06:28:42Z) - MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D
Segmentation [91.6658845016214]
We propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks.
We render a 3D shape from multiple views, and set up a dense correspondence learning task within the contrastive learning framework.
As a result, the learned 2D representations are view-invariant and geometrically consistent.
arXiv Detail & Related papers (2022-08-18T00:48:15Z) - DM-NeRF: 3D Scene Geometry Decomposition and Manipulation from 2D Images [15.712721653893636]
DM-NeRF is among the first to simultaneously reconstruct, decompose, manipulate and render complex 3D scenes in a single pipeline.
Our method can accurately decompose all 3D objects from 2D views, allowing any interested object to be freely manipulated in 3D space.
arXiv Detail & Related papers (2022-08-15T14:32:10Z) - 3D Shape Reconstruction from Free-Hand Sketches [42.15888734492648]
Despite great progress achieved in 3D reconstruction from distortion-free line drawings, little effort has been made to reconstruct 3D shapes from free-hand sketches.
We aim to enhance the power of sketches in 3D-related applications such as interactive design and VR/AR games.
A major challenge for free-hand sketch 3D reconstruction comes from the insufficient training data and free-hand sketch diversity.
arXiv Detail & Related papers (2020-06-17T07:43:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.