LISA: Learning Implicit Shape and Appearance of Hands

• URL: http://arxiv.org/abs/2204.01695v1
• Date: Mon, 4 Apr 2022 17:59:03 GMT
• Title: LISA: Learning Implicit Shape and Appearance of Hands
• Authors: Enric Corona, Tomas Hodan, Minh Vo, Francesc Moreno-Noguer, Chris Sweeney, Richard Newcombe, Lingni Ma
• Abstract summary: This paper proposes a do-it-all neural model of human hands, named LISA. The model can capture accurate hand shape and appearance, generalize to arbitrary hand subjects, provide dense surface correspondences, be reconstructed from images in the wild and easily animated.
• Score: 33.477530463242275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a do-it-all neural model of human hands, named LISA. The model can capture accurate hand shape and appearance, generalize to arbitrary hand subjects, provide dense surface correspondences, be reconstructed from images in the wild and easily animated. We train LISA by minimizing the shape and appearance losses on a large set of multi-view RGB image sequences annotated with coarse 3D poses of the hand skeleton. For a 3D point in the hand local coordinate, our model predicts the color and the signed distance with respect to each hand bone independently, and then combines the per-bone predictions using predicted skinning weights. The shape, color and pose representations are disentangled by design, allowing to estimate or animate only selected parameters. We experimentally demonstrate that LISA can accurately reconstruct a dynamic hand from monocular or multi-view sequences, achieving a noticeably higher quality of reconstructed hand shapes compared to baseline approaches. Project page: https://www.iri.upc.edu/people/ecorona/lisa/.

Related papers

• Towards Human-Level 3D Relative Pose Estimation: Generalizable, Training-Free, with Single Reference [62.99706119370521]
  Humans can easily deduce the relative pose of an unseen object, without label/training, given only a single query-reference image pair. We propose a novel 3D generalizable relative pose estimation method by elaborating (i) with a 2.5D shape from an RGB-D reference, (ii) with an off-the-shelf differentiable, and (iii) with semantic cues from a pretrained model like DINOv2.
  arXiv  Detail & Related papers  (2024-06-26T16:01:10Z)
• HandNeRF: Neural Radiance Fields for Animatable Interacting Hands [122.32855646927013]
  We propose a novel framework to reconstruct accurate appearance and geometry with neural radiance fields (NeRF) for interacting hands. We conduct extensive experiments to verify the merits of our proposed HandNeRF and report a series of state-of-the-art results.
  arXiv  Detail & Related papers  (2023-03-24T06:19:19Z)
• NIMBLE: A Non-rigid Hand Model with Bones and Muscles [41.19718491215149]
  We present NIMBLE, a novel parametric hand model that includes the missing key components. NIMBLE consists of 20 bones as triangular meshes, 7 muscle groups as tetrahedral meshes, and a skin mesh. We demonstrate applying NIMBLE to modeling, rendering, and visual inference tasks.
  arXiv  Detail & Related papers  (2022-02-09T15:57:21Z)
• 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)
• Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentation [84.28064034301445]
  Self-similarity, and the resulting ambiguities in assigning pixel observations to the respective hands, is a major cause of the final 3D pose error. We propose DIGIT, a novel method for estimating the 3D poses of two interacting hands from a single monocular image. We experimentally show that the proposed approach achieves new state-of-the-art performance on the InterHand2.6M dataset.
  arXiv  Detail & Related papers  (2021-07-01T13:28:02Z)
• Monocular Real-time Full Body Capture with Inter-part Correlations [66.22835689189237]
  We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image. Our approach uses a new neural network architecture that exploits correlations between body and hands at high computational efficiency.
  arXiv  Detail & Related papers  (2020-12-11T02:37:56Z)
• Learning an Animatable Detailed 3D Face Model from In-The-Wild Images [50.09971525995828]
  We present the first approach to jointly learn a model with animatable detail and a detailed 3D face regressor from in-the-wild images. Our DECA model is trained to robustly produce a UV displacement map from a low-dimensional latent representation. We introduce a novel detail-consistency loss to disentangle person-specific details and expression-dependent wrinkles.
  arXiv  Detail & Related papers  (2020-12-07T19:30:45Z)
• Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis [72.34794624243281]
  We propose a self-supervised learning framework to disentangle variations from unlabeled video frames. Our differentiable formalization, bridging the representation gap between the 3D pose and spatial part maps, allows us to operate on videos with diverse camera movements.
  arXiv  Detail & Related papers  (2020-04-09T07:55:01Z)

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.