DifferSketching: How Differently Do People Sketch 3D Objects?

• URL: http://arxiv.org/abs/2209.08791v1
• Date: Mon, 19 Sep 2022 06:52:18 GMT
• Title: DifferSketching: How Differently Do People Sketch 3D Objects?
• Authors: Chufeng Xiao, Wanchao Su, Jing Liao, Zhouhui Lian, Yi-Zhe Song, Hongbo Fu
• Abstract summary: Multiple sketch datasets have been proposed to understand how people draw 3D objects. These datasets are often of small scale and cover a small set of objects or categories. We analyze the collected data at three levels, i.e., sketch-level, stroke-level, and pixel-level, under both spatial and temporal characteristics.
• Score: 78.44544977215918
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiple sketch datasets have been proposed to understand how people draw 3D objects. However, such datasets are often of small scale and cover a small set of objects or categories. In addition, these datasets contain freehand sketches mostly from expert users, making it difficult to compare the drawings by expert and novice users, while such comparisons are critical in informing more effective sketch-based interfaces for either user groups. These observations motivate us to analyze how differently people with and without adequate drawing skills sketch 3D objects. We invited 70 novice users and 38 expert users to sketch 136 3D objects, which were presented as 362 images rendered from multiple views. This leads to a new dataset of 3,620 freehand multi-view sketches, which are registered with their corresponding 3D objects under certain views. Our dataset is an order of magnitude larger than the existing datasets. We analyze the collected data at three levels, i.e., sketch-level, stroke-level, and pixel-level, under both spatial and temporal characteristics, and within and across groups of creators. We found that the drawings by professionals and novices show significant differences at stroke-level, both intrinsically and extrinsically. We demonstrate the usefulness of our dataset in two applications: (i) freehand-style sketch synthesis, and (ii) posing it as a potential benchmark for sketch-based 3D reconstruction. Our dataset and code are available at https://chufengxiao.github.io/DifferSketching/.

Related papers

• SketchTriplet: Self-Supervised Scenarized Sketch-Text-Image Triplet Generation [6.39528707908268]
  There continues to be a lack of large-scale paired datasets for scene sketches. We propose a self-supervised method for scene sketch generation that does not rely on any existing scene sketch. We contribute a large-scale dataset centered around scene sketches, comprising highly semantically consistent "text-sketch-image" triplets.
  arXiv  Detail & Related papers  (2024-05-29T06:43:49Z)
• 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)
• Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats [80.12253291709673]
  We propose a novel affine-combining autoencoder (ACAE) method to perform dimensionality reduction on the number of landmarks. Our approach scales to an extreme multi-dataset regime, where we use 28 3D human pose datasets to supervise one model.
  arXiv  Detail & Related papers  (2022-12-29T22:22:49Z)
• Fine-Grained VR Sketching: Dataset and Insights [140.0579567561475]
  We present the first fine-grained dataset of 1,497 3D VR sketch and 3D shape pairs of a chair category with large shapes diversity. Our dataset supports the recent trend in the sketch community on fine-grained data analysis.
  arXiv  Detail & Related papers  (2022-09-20T21:30:54Z)
• Interactive Annotation of 3D Object Geometry using 2D Scribbles [84.51514043814066]
  In this paper, we propose an interactive framework for annotating 3D object geometry from point cloud data and RGB imagery. Our framework targets naive users without artistic or graphics expertise.
  arXiv  Detail & Related papers  (2020-08-24T21:51:29Z)
• SketchDesc: Learning Local Sketch Descriptors for Multi-view Correspondence [68.63311821718416]
  We study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object. This problem is challenging since the visual features of corresponding points at different views can be very different. We take a deep learning approach and learn a novel local sketch descriptor from data.
  arXiv  Detail & Related papers  (2020-01-16T11:31:21Z)

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.