Summary of Airsketch: Generative Motion to Sketch, by Hui Xian Grace Lim et al.
AirSketch: Generative Motion to Sketch
by Hui Xian Grace Lim, Xuanming Cui, Ser-Nam Lim, Yogesh S Rawat
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces AirSketch, a concept that enables the generation of faithful and visually coherent sketches directly from hand motions. Unlike existing Augmented and Virtual Reality technologies (AR/VR) that require costly hardware and digital markers, AirSketch eliminates the need for these tools. The authors develop a simple augmentation-based self-supervised training procedure to train a controllable image diffusion model to translate noisy hand tracking images into clean, aesthetically pleasing sketches while preserving visual cues from the original data. Two air drawing datasets are presented to study this problem. The findings demonstrate that controllable image diffusion can produce refined, clear sketches from noisy inputs beyond generating photo-realistic images from precise spatial inputs. This work serves as an initial step towards marker-less air drawing and reveals distinct applications of controllable diffusion models to AirSketch and AR/VR in general. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about creating a new way to draw pictures using just your hands. The current technology, Augmented Reality (AR), requires special equipment like headsets or markers. This new method, called AirSketch, lets you create drawings without any extra gear. The researchers developed a way for computers to learn how to turn hand movements into clear and nice-looking drawings. They tested this with two sets of hand movement data and found that it works well. This technology has the potential to make drawing more accessible and could be used in virtual reality too. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Self supervised * Tracking
