NutritionVerse-Synth: An Open Access Synthetically Generated 2D Food Scene Dataset for Dietary Intake Estimation

• URL: http://arxiv.org/abs/2312.06192v1
• Date: Mon, 11 Dec 2023 08:15:49 GMT
• Title: NutritionVerse-Synth: An Open Access Synthetically Generated 2D Food Scene Dataset for Dietary Intake Estimation
• Authors: Saeejith Nair, Chi-en Amy Tai, Yuhao Chen, Alexander Wong
• Abstract summary: We introduce NutritionVerse- Synth (NV- Synth), a large-scale synthetic food image dataset. NV- Synth contains 84,984 photorealistic meal images rendered from 7,082 dynamically plated 3D scenes. As the largest open-source synthetic food dataset, NV- Synth highlights the value of physics-based simulations.
• Score: 71.22646949733833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Manually tracking nutritional intake via food diaries is error-prone and burdensome. Automated computer vision techniques show promise for dietary monitoring but require large and diverse food image datasets. To address this need, we introduce NutritionVerse-Synth (NV-Synth), a large-scale synthetic food image dataset. NV-Synth contains 84,984 photorealistic meal images rendered from 7,082 dynamically plated 3D scenes. Each scene is captured from 12 viewpoints and includes perfect ground truth annotations such as RGB, depth, semantic, instance, and amodal segmentation masks, bounding boxes, and detailed nutritional information per food item. We demonstrate the diversity of NV-Synth across foods, compositions, viewpoints, and lighting. As the largest open-source synthetic food dataset, NV-Synth highlights the value of physics-based simulations for enabling scalable and controllable generation of diverse photorealistic meal images to overcome data limitations and drive advancements in automated dietary assessment using computer vision. In addition to the dataset, the source code for our data generation framework is also made publicly available at https://saeejithnair.github.io/nvsynth.

Related papers

• MetaFood3D: Large 3D Food Object Dataset with Nutrition Values [53.24500333363066]
  This dataset consists of 637 meticulously labeled 3D food objects across 108 categories, featuring detailed nutrition information, weight, and food codes linked to a comprehensive nutrition database. Experimental results demonstrate our dataset's significant potential for improving algorithm performance, highlight the challenging gap between video captures and 3D scanned data, and show the strength of the MetaFood3D dataset in high-quality data generation, simulation, and augmentation.
  arXiv  Detail & Related papers  (2024-09-03T15:02:52Z)
• FoodFusion: A Latent Diffusion Model for Realistic Food Image Generation [69.91401809979709]
  Current state-of-the-art image generation models such as Latent Diffusion Models (LDMs) have demonstrated the capacity to produce visually striking food-related images. We introduce FoodFusion, a Latent Diffusion model engineered specifically for the faithful synthesis of realistic food images from textual descriptions. The development of the FoodFusion model involves harnessing an extensive array of open-source food datasets, resulting in over 300,000 curated image-caption pairs.
  arXiv  Detail & Related papers  (2023-12-06T15:07:12Z)
• NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene Dataset for Dietary Intake Estimation [68.49526750115429]
  We introduce NutritionVerse-Real, an open access manually collected 2D food scene dataset for dietary intake estimation. The NutritionVerse-Real dataset was created by manually collecting images of food scenes in real life, measuring the weight of every ingredient and computing the associated dietary content of each dish.
  arXiv  Detail & Related papers  (2023-11-20T11:05:20Z)
• NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches [59.38343165508926]
  Accurate dietary intake estimation is critical for informing policies and programs to support healthy eating. Recent work has focused on using computer vision and machine learning to automatically estimate dietary intake from food images. We introduce NutritionVerse- Synth, the first large-scale dataset of 84,984 synthetic 2D food images with associated dietary information. We also collect a real image dataset, NutritionVerse-Real, containing 889 images of 251 dishes to evaluate realism.
  arXiv  Detail & Related papers  (2023-09-14T13:29:41Z)
• NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation [65.47310907481042]
  One in four older adults are malnourished. Machine learning and computer vision show promise of automated nutrition tracking methods of food. NutritionVerse-3D is a large-scale high-resolution dataset of 105 3D food models.
  arXiv  Detail & Related papers  (2023-04-12T05:27:30Z)

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.