AnthroNet: Conditional Generation of Humans via Anthropometrics

• URL: http://arxiv.org/abs/2309.03812v1
• Date: Thu, 7 Sep 2023 16:09:06 GMT
• Title: AnthroNet: Conditional Generation of Humans via Anthropometrics
• Authors: Francesco Picetti, Shrinath Deshpande, Jonathan Leban, Soroosh Shahtalebi, Jay Patel, Peifeng Jing, Chunpu Wang, Charles Metze III, Cameron Sun, Cera Laidlaw, James Warren, Kathy Huynh, River Page, Jonathan Hogins, Adam Crespi, Sujoy Ganguly, Salehe Erfanian Ebadi
• Abstract summary: We present a novel human body model formulated by an extensive set of anthropocentric measurements. The proposed model enables direct modeling of specific human identities through a deep generative architecture. It is the first of its kind to have been trained end-to-end using only synthetically generated data.
• Score: 2.4016781107988265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel human body model formulated by an extensive set of anthropocentric measurements, which is capable of generating a wide range of human body shapes and poses. The proposed model enables direct modeling of specific human identities through a deep generative architecture, which can produce humans in any arbitrary pose. It is the first of its kind to have been trained end-to-end using only synthetically generated data, which not only provides highly accurate human mesh representations but also allows for precise anthropometry of the body. Moreover, using a highly diverse animation library, we articulated our synthetic humans' body and hands to maximize the diversity of the learnable priors for model training. Our model was trained on a dataset of $100k$ procedurally-generated posed human meshes and their corresponding anthropometric measurements. Our synthetic data generator can be used to generate millions of unique human identities and poses for non-commercial academic research purposes.

Related papers

• HINT: Learning Complete Human Neural Representations from Limited Viewpoints [69.76947323932107]
  We propose a NeRF-based algorithm able to learn a detailed and complete human model from limited viewing angles. As a result, our method can reconstruct complete humans even from a few viewing angles, increasing performance by more than 15% PSNR.
  arXiv  Detail & Related papers  (2024-05-30T05:43:09Z)
• SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling [93.60731530276911]
  We introduce a new synthetic dataset, SynBody, with three appealing features. The dataset comprises 1.2M images with corresponding accurate 3D annotations, covering 10,000 human body models, 1,187 actions, and various viewpoints.
  arXiv  Detail & Related papers  (2023-03-30T13:30:12Z)
• Procedural Humans for Computer Vision [1.9550079119934403]
  We build a parametric model of the face and body, including articulated hands, to generate realistic images of humans based on this body model. We show that this can be extended to include the full body by building on the pipeline of Wood et al. to generate synthetic images of humans in their entirety.
  arXiv  Detail & Related papers  (2023-01-03T15:44:48Z)
• gDNA: Towards Generative Detailed Neural Avatars [94.9804106939663]
  We show that our model is able to generate natural human avatars wearing diverse and detailed clothing. Our method can be used on the task of fitting human models to raw scans, outperforming the previous state-of-the-art.
  arXiv  Detail & Related papers  (2022-01-11T18:46:38Z)
• HSPACE: Synthetic Parametric Humans Animated in Complex Environments [67.8628917474705]
  We build a large-scale photo-realistic dataset, Human-SPACE, of animated humans placed in complex indoor and outdoor environments. We combine a hundred diverse individuals of varying ages, gender, proportions, and ethnicity, with hundreds of motions and scenes, in order to generate an initial dataset of over 1 million frames. Assets are generated automatically, at scale, and are compatible with existing real time rendering and game engines.
  arXiv  Detail & Related papers  (2021-12-23T22:27:55Z)
• Automatic Estimation of Anthropometric Human Body Measurements [0.0]
  This paper formulates a research in the field of deep learning and neural networks, to tackle the challenge of body measurements estimation from various types of visual input data. Also, we deal with the lack of real human data annotated with ground truth body measurements required for training and evaluation, by generating a synthetic dataset of various human body shapes.
  arXiv  Detail & Related papers  (2021-12-22T16:13:59Z)
• LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies [78.17425779503047]
  We propose a novel neural implicit representation for the human body. It is fully differentiable and optimizable with disentangled shape and pose latent spaces. Our model can be trained and fine-tuned directly on non-watertight raw data with well-designed losses.
  arXiv  Detail & Related papers  (2021-11-30T04:10:57Z)
• imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose [42.4185273307021]
  We present imGHUM, the first holistic generative model of 3D human shape and articulated pose. We model the full human body implicitly as a function zero-level-set and without the use of an explicit template mesh.
  arXiv  Detail & Related papers  (2021-08-24T17:08:28Z)
• S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling [103.65625425020129]
  We represent the pedestrian's shape, pose and skinning weights as neural implicit functions that are directly learned from data. We demonstrate the effectiveness of our approach on various datasets and show that our reconstructions outperform existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-01-17T02:16:56Z)

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.