SketchINR: A First Look into Sketches as Implicit Neural Representations

• URL: http://arxiv.org/abs/2403.09344v1
• Date: Thu, 14 Mar 2024 12:49:29 GMT
• Title: SketchINR: A First Look into Sketches as Implicit Neural Representations
• Authors: Hmrishav Bandyopadhyay, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain, Tao Xiang, Timothy Hospedales, Yi-Zhe Song,
• Abstract summary: We propose SketchINR, to advance the representation of vector sketches with implicit neural models. A variable length vector sketch is compressed into a latent space of fixed dimension that implicitly encodes the underlying shape as a function of time and strokes. For the first time, SketchINR emulates the human ability to reproduce a sketch with varying abstraction in terms of number and complexity of strokes.
• Score: 120.4152701687737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose SketchINR, to advance the representation of vector sketches with implicit neural models. A variable length vector sketch is compressed into a latent space of fixed dimension that implicitly encodes the underlying shape as a function of time and strokes. The learned function predicts the $xy$ point coordinates in a sketch at each time and stroke. Despite its simplicity, SketchINR outperforms existing representations at multiple tasks: (i) Encoding an entire sketch dataset into a fixed size latent vector, SketchINR gives $60\times$ and $10\times$ data compression over raster and vector sketches, respectively. (ii) SketchINR's auto-decoder provides a much higher-fidelity representation than other learned vector sketch representations, and is uniquely able to scale to complex vector sketches such as FS-COCO. (iii) SketchINR supports parallelisation that can decode/render $\sim$$100\times$ faster than other learned vector representations such as SketchRNN. (iv) SketchINR, for the first time, emulates the human ability to reproduce a sketch with varying abstraction in terms of number and complexity of strokes. As a first look at implicit sketches, SketchINR's compact high-fidelity representation will support future work in modelling long and complex sketches.

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)
• Abstracting Sketches through Simple Primitives [53.04827416243121]
  Humans show high-level of abstraction capabilities in games that require quickly communicating object information. We propose the Primitive-based Sketch Abstraction task where the goal is to represent sketches using a fixed set of drawing primitives. Our Primitive-Matching Network (PMN), learns interpretable abstractions of a sketch in a self supervised manner.
  arXiv  Detail & Related papers  (2022-07-27T14:32:39Z)
• FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context [112.07988211268612]
  We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. Our dataset comprises 10,000 freehand scene vector sketches with per point space-time information by 100 non-expert individuals. We study for the first time the problem of the fine-grained image retrieval from freehand scene sketches and sketch captions.
  arXiv  Detail & Related papers  (2022-03-04T03:00:51Z)
• Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel [15.535749953841274]
  Randomized sketches of a product of $p$ vectors follow a tradeoff between statistical efficiency and computational acceleration. We propose a simple Complex-to-Real (CtR) modification of well-known sketches that replaces real random projections by complex ones. We show that our method achieves state-of-the-art performance in terms of accuracy and speed compared to other randomized approximations from the literature.
  arXiv  Detail & Related papers  (2022-02-04T09:15:43Z)
• Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketches [65.96417928860039]
  We use an encoder/decoder architecture for the sketch to mesh translation. We will show that this approach is easy to deploy, robust to style changes, and effective.
  arXiv  Detail & Related papers  (2021-04-01T14:10:59Z)
• B\'ezierSketch: A generative model for scalable vector sketches [132.5223191478268]
  We present B'ezierSketch, a novel generative model for fully vector sketches that are automatically scalable and high-resolution. We first introduce a novel inverse graphics approach to stroke embedding that trains an encoder to embed each stroke to its best fit B'ezier curve. This enables us to treat sketches as short sequences of paramaterized strokes and thus train a recurrent sketch generator with greater capacity for longer sketches.
  arXiv  Detail & Related papers  (2020-07-04T21:30:52Z)
• Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt [125.17887147597567]
  We present a model of learning Sketch BiBERT Representation from Transformer (Sketch-BERT) We generalize BERT to sketch domain, with the novel proposed components and pre-training algorithms. We show that the learned representation of Sketch-BERT can help and improve the performance of the downstream tasks of sketch recognition, sketch retrieval, and sketch gestalt.
  arXiv  Detail & Related papers  (2020-05-19T01:35:44Z)
• Sketchformer: Transformer-based Representation for Sketched Structure [12.448155157592895]
  Sketchformer is a transformer-based representation for encoding free-hand sketches input in a vector form. We report several variants exploring continuous and tokenized input representations, and contrast their performance. Our learned embedding, driven by a dictionary learning tokenization scheme, yields state of the art performance in classification and image retrieval tasks.
  arXiv  Detail & Related papers  (2020-02-24T17:11:53Z)

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.