Self Supervised Networks for Learning Latent Space Representations of Human Body Scans and Motions

• URL: http://arxiv.org/abs/2411.03475v1
• Date: Tue, 05 Nov 2024 19:59:40 GMT
• Title: Self Supervised Networks for Learning Latent Space Representations of Human Body Scans and Motions
• Authors: Emmanuel Hartman, Nicolas Charon, Martin Bauer,
• Abstract summary: This paper introduces self-supervised neural network models to tackle several fundamental problems in the field of 3D human body analysis and processing. We propose VariShaPE, a novel architecture for the retrieval of latent space representations of body shapes and poses. Second, we complement the estimation of latent codes with MoGeN, a framework that learns the geometry on the latent space itself.
• Score: 6.165163123577484
• License:
• Abstract: This paper introduces self-supervised neural network models to tackle several fundamental problems in the field of 3D human body analysis and processing. First, we propose VariShaPE (Varifold Shape Parameter Estimator), a novel architecture for the retrieval of latent space representations of body shapes and poses. This network offers a fast and robust method to estimate the embedding of arbitrary unregistered meshes into the latent space. Second, we complement the estimation of latent codes with MoGeN (Motion Geometry Network) a framework that learns the geometry on the latent space itself. This is achieved by lifting the body pose parameter space into a higher dimensional Euclidean space in which body motion mini-sequences from a training set of 4D data can be approximated by simple linear interpolation. Using the SMPL latent space representation we illustrate how the combination of these network models, once trained, can be used to perform a variety of tasks with very limited computational cost. This includes operations such as motion interpolation, extrapolation and transfer as well as random shape and pose generation.

Related papers

• Large Spatial Model: End-to-end Unposed Images to Semantic 3D [79.94479633598102]
  Large Spatial Model (LSM) processes unposed RGB images directly into semantic radiance fields. LSM simultaneously estimates geometry, appearance, and semantics in a single feed-forward operation. It can generate versatile label maps by interacting with language at novel viewpoints.
  arXiv  Detail & Related papers  (2024-10-24T17:54:42Z)
• VortSDF: 3D Modeling with Centroidal Voronoi Tesselation on Signed Distance Field [5.573454319150408]
  We introduce a volumetric optimization framework that combines explicit SDF fields with a shallow color network, in order to estimate 3D shape properties over tetrahedral grids. Experimental results with Chamfer statistics validate this approach with unprecedented reconstruction quality on various scenarios such as objects, open scenes or human.
  arXiv  Detail & Related papers  (2024-07-29T09:46:39Z)
• Simplicits: Mesh-Free, Geometry-Agnostic, Elastic Simulation [18.45850302604534]
  We present a data-, mesh-, and grid-free solution for elastic simulation for any object in any geometric representation. For each object, we fit a small implicit neural network encoding varying weights that act as a reduced deformation basis. Our experiments demonstrate the versatility, accuracy, and speed of this approach on data including signed distance functions, point clouds, neural primitives, tomography scans, radiance fields, Gaussian splats, surface meshes, and volume meshes.
  arXiv  Detail & Related papers  (2024-06-09T18:57:23Z)
• High-fidelity 3D Model Compression based on Key Spheres [6.59007277780362]
  We propose an SDF prediction network using explicit key spheres as input. Our method achieves the high-fidelity and high-compression 3D object coding and reconstruction.
  arXiv  Detail & Related papers  (2022-01-19T09:21:54Z)
• 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)
• Scene Synthesis via Uncertainty-Driven Attribute Synchronization [52.31834816911887]
  This paper introduces a novel neural scene synthesis approach that can capture diverse feature patterns of 3D scenes. Our method combines the strength of both neural network-based and conventional scene synthesis approaches.
  arXiv  Detail & Related papers  (2021-08-30T19:45:07Z)
• NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go [109.88509362837475]
  We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes. NeuroMorph produces smooth and point-to-point correspondences between them. It works well for a large variety of input shapes, including non-isometric pairs from different object categories.
  arXiv  Detail & Related papers  (2021-06-17T12:25:44Z)
• Spatially Invariant Unsupervised 3D Object Segmentation with Graph Neural Networks [23.729853358582506]
  We propose a framework, SPAIR3D, to model a point cloud as a spatial mixture model. We jointly learn the multiple-object representation and segmentation in 3D via Variational Autoencoders (VAE) Experimental results demonstrate that SPAIR3D is capable of detecting and segmenting variable number of objects without appearance information.
  arXiv  Detail & Related papers  (2021-06-10T09:20:16Z)
• Locally Aware Piecewise Transformation Fields for 3D Human Mesh Registration [67.69257782645789]
  We propose piecewise transformation fields that learn 3D translation vectors to map any query point in posed space to its correspond position in rest-pose space. We show that fitting parametric models with poses by our network results in much better registration quality, especially for extreme poses.
  arXiv  Detail & Related papers  (2021-04-16T15:16:09Z)
• Learning Transferable Kinematic Dictionary for 3D Human Pose and Shape Reconstruction [15.586347115568973]
  We propose a kinematic dictionary, which explicitly regularizes the solution space of relative 3D rotations of human joints. Our method achieves end-to-end 3D reconstruction without the need of using any shape annotations during the training of neural networks. The proposed method achieves competitive results on large-scale datasets including Human3.6M, MPI-INF-3DHP, and LSP.
  arXiv  Detail & Related papers  (2021-04-02T09:24:29Z)
• Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction [123.62341095156611]
  Implicit functions represented as deep learning approximations are powerful for reconstructing 3D surfaces. Such features are essential in building flexible models for both computer graphics and computer vision. We present methodology that combines detail-rich implicit functions and parametric representations.
  arXiv  Detail & Related papers  (2020-07-22T13:46:14Z)

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.