implicit
Papers with tag implicit
2022
- FIND: An Unsupervised Implicit 3D Model of Articulated Human FeetOliver Boyne, James Charles, and Roberto CipollaIn 2022
In this paper we present a high fidelity and articulated 3D human foot model.The model is parameterised by a disentangled latent code in terms of shape,texture and articulated pose. While high fidelity models are typically createdwith strong supervision such as 3D keypoint correspondences orpre-registration, we focus on the difficult case of little to no annotation. Tothis end, we make the following contributions: (i) we develop a Foot ImplicitNeural Deformation field model, named FIND, capable of tailoring explicitmeshes at any resolution i.e. for low or high powered devices; (ii) an approachfor training our model in various modes of weak supervision with progressivelybetter disentanglement as more labels, such as pose categories, are provided;(iii) a novel unsupervised part-based loss for fitting our model to 2D imageswhich is better than traditional photometric or silhouette losses; (iv)finally, we release a new dataset of high resolution 3D human foot scans,Foot3D. On this dataset, we show our model outperforms a strong PCAimplementation trained on the same data in terms of shape quality and partcorrespondences, and that our novel unsupervised part-based loss improvesinference on images.
使用RGB来自监督的训练脚的隐式表达
human feet human-representation implicit-model@inproceedings{FIND, title = {FIND: An Unsupervised Implicit 3D Model of Articulated Human Feet}, author = {Boyne, Oliver and Charles, James and Cipolla, Roberto}, year = {2022}, tags = {human, feet, human-representation, implicit-model}, } - ARAH: Animatable Volume Rendering of Articulated Human SDFsShaofei Wang, Katja Schwarz, Andreas Geiger, and Siyu TangIn 2022
Combining human body models with differentiable rendering has recentlyenabled animatable avatars of clothed humans from sparse sets of multi-view RGBvideos. While state-of-the-art approaches achieve realistic appearance withneural radiance fields (NeRF), the inferred geometry often lacks detail due tomissing geometric constraints. Further, animating avatars inout-of-distribution poses is not yet possible because the mapping fromobservation space to canonical space does not generalize faithfully to unseenposes. In this work, we address these shortcomings and propose a model tocreate animatable clothed human avatars with detailed geometry that generalizewell to out-of-distribution poses. To achieve detailed geometry, we combine anarticulated implicit surface representation with volume rendering. Forgeneralization, we propose a novel joint root-finding algorithm forsimultaneous ray-surface intersection search and correspondence search. Ouralgorithm enables efficient point sampling and accurate point canonicalizationwhile generalizing well to unseen poses. We demonstrate that our proposedpipeline can generate clothed avatars with high-quality pose-dependent geometryand appearance from a sparse set of multi-view RGB videos. Our method achievesstate-of-the-art performance on geometry and appearance reconstruction whilecreating animatable avatars that generalize well to out-of-distribution posesbeyond the small number of training poses.
clothed implicit neural-rendering@inproceedings{ARAH, title = {ARAH: Animatable Volume Rendering of Articulated Human SDFs}, author = {Wang, Shaofei and Schwarz, Katja and Geiger, Andreas and Tang, Siyu}, year = {2022}, tags = {clothed, implicit, neural-rendering}, }