A Human Ear Reconstruction Autoencoder

• URL: http://arxiv.org/abs/2010.03972v1
• Date: Wed, 7 Oct 2020 12:52:23 GMT
• Title: A Human Ear Reconstruction Autoencoder
• Authors: Hao Sun, Nick Pears and Hang Dai
• Abstract summary: We aim to tackle the 3D ear reconstruction task, where more subtle and difficult curves and features are present on the 2D ear input images. Our Human Ear Reconstruction Autoencoder (HERA) system predicts 3D ear poses and shape parameters for 3D ear meshes, without any supervision to these parameters. The constructed end-to-end self-supervised model is then evaluated both with 2D landmark localisation performance and the appearance of the reconstructed 3D ears.
• Score: 19.72707659069644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ear, as an important part of the human head, has received much less attention compared to the human face in the area of computer vision. Inspired by previous work on monocular 3D face reconstruction using an autoencoder structure to achieve self-supervised learning, we aim to utilise such a framework to tackle the 3D ear reconstruction task, where more subtle and difficult curves and features are present on the 2D ear input images. Our Human Ear Reconstruction Autoencoder (HERA) system predicts 3D ear poses and shape parameters for 3D ear meshes, without any supervision to these parameters. To make our approach cover the variance for in-the-wild images, even grayscale images, we propose an in-the-wild ear colour model. The constructed end-to-end self-supervised model is then evaluated both with 2D landmark localisation performance and the appearance of the reconstructed 3D ears.

Related papers

• Head Pose Estimation and 3D Neural Surface Reconstruction via Monocular Camera in situ for Navigation and Safe Insertion into Natural Openings [4.592222359553848]
  We have chosen 3D Slicer as our base platform and monocular cameras are used as sensors. We used the neural radiance fields (NeRF) algorithm to complete the 3D model reconstruction of the human head. The individual's head pose, obtained through single-camera vision, is transmitted in real-time to the scene created within 3D Slicer.
  arXiv  Detail & Related papers  (2024-06-18T20:42:09Z)
• Talk3D: High-Fidelity Talking Portrait Synthesis via Personalized 3D Generative Prior [29.120669908374424]
  We introduce a novel audio-driven talking head synthesis framework, called Talk3D. It can faithfully reconstruct its plausible facial geometries by effectively adopting the pre-trained 3D-aware generative prior. Compared to existing methods, our method excels in generating realistic facial geometries even under extreme head poses.
  arXiv  Detail & Related papers  (2024-03-29T12:49:40Z)
• HeadArtist: Text-conditioned 3D Head Generation with Self Score Distillation [95.58892028614444]
  This work presents HeadArtist for 3D head generation from text descriptions. We come up with an efficient pipeline that optimize a parameterized 3D head model under the supervision of the prior distillation. Experimental results suggest that our approach delivers high-quality 3D head sculptures with adequate geometry and photorealistic appearance.
  arXiv  Detail & Related papers  (2023-12-12T18:59:25Z)
• MinD-3D: Reconstruct High-quality 3D objects in Human Brain [50.534007259536715]
  Recon3DMind is an innovative task aimed at reconstructing 3D visuals from Functional Magnetic Resonance Imaging (fMRI) signals. We present the fMRI-Shape dataset, which includes data from 14 participants and features 360-degree videos of 3D objects. We propose MinD-3D, a novel and effective three-stage framework specifically designed to decode the brain's 3D visual information from fMRI signals.
  arXiv  Detail & Related papers  (2023-12-12T18:21:36Z)
• PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360$^{\circ}$ [17.355141949293852]
  Existing 3D generative adversarial networks (GANs) for 3D human head synthesis are either limited to near-frontal views or hard to preserve 3D consistency in large view angles. We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in $360circ$ with diverse appearance and detailed geometry.
  arXiv  Detail & Related papers  (2023-03-23T06:54:34Z)
• AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio [44.460995595847606]
  We propose to achieve personalized spatial audio by reconstructing 3D human ears with single-view images. To fill the gap between the vision and acoustics community, we develop a pipeline to integrate the reconstructed ear mesh with an off-the-shelf 3D human body.
  arXiv  Detail & Related papers  (2023-01-30T02:15:50Z)
• DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars [92.37436369781692]
  We present DRaCoN, a framework for learning full-body volumetric avatars. It exploits the advantages of both the 2D and 3D neural rendering techniques. Experiments on the challenging ZJU-MoCap and Human3.6M datasets indicate that DRaCoN outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T17:59:15Z)
• StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision [54.920605385622274]
  We propose StereoPIFu, which integrates the geometric constraints of stereo vision with implicit function representation of PIFu, to recover the 3D shape of the clothed human. Compared with previous works, our StereoPIFu significantly improves the robustness, completeness, and accuracy of the clothed human reconstruction.
  arXiv  Detail & Related papers  (2021-04-12T08:41:54Z)
• Model-based 3D Hand Reconstruction via Self-Supervised Learning [72.0817813032385]
  Reconstructing a 3D hand from a single-view RGB image is challenging due to various hand configurations and depth ambiguity. We propose S2HAND, a self-supervised 3D hand reconstruction network that can jointly estimate pose, shape, texture, and the camera viewpoint. For the first time, we demonstrate the feasibility of training an accurate 3D hand reconstruction network without relying on manual annotations.
  arXiv  Detail & Related papers  (2021-03-22T10:12:43Z)
• Neural Descent for Visual 3D Human Pose and Shape [67.01050349629053]
  We present deep neural network methodology to reconstruct the 3d pose and shape of people, given an input RGB image. We rely on a recently introduced, expressivefull body statistical 3d human model, GHUM, trained end-to-end. Central to our methodology, is a learning to learn and optimize approach, referred to as HUmanNeural Descent (HUND), which avoids both second-order differentiation.
  arXiv  Detail & Related papers  (2020-08-16T13:38:41Z)

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.