RigNet: Neural Rigging for Articulated Characters

• URL: http://arxiv.org/abs/2005.00559v2
• Date: Sun, 5 Jul 2020 19:38:56 GMT
• Title: RigNet: Neural Rigging for Articulated Characters
• Authors: Zhan Xu, Yang Zhou, Evangelos Kalogerakis, Chris Landreth and Karan Singh
• Abstract summary: RigNet is an end-to-end automated method for producing animation rigs from input character models. It predicts a skeleton that matches the animator expectations in joint placement and topology. It also estimates surface skin weights based on the predicted skeleton.
• Score: 34.46896139582373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present RigNet, an end-to-end automated method for producing animation rigs from input character models. Given an input 3D model representing an articulated character, RigNet predicts a skeleton that matches the animator expectations in joint placement and topology. It also estimates surface skin weights based on the predicted skeleton. Our method is based on a deep architecture that directly operates on the mesh representation without making assumptions on shape class and structure. The architecture is trained on a large and diverse collection of rigged models, including their mesh, skeletons and corresponding skin weights. Our evaluation is three-fold: we show better results than prior art when quantitatively compared to animator rigs; qualitatively we show that our rigs can be expressively posed and animated at multiple levels of detail; and finally, we evaluate the impact of various algorithm choices on our output rigs.

Related papers

• SkelFormer: Markerless 3D Pose and Shape Estimation using Skeletal Transformers [57.46911575980854]
  We introduce SkelFormer, a novel markerless motion capture pipeline for multi-view human pose and shape estimation. Our method first uses off-the-shelf 2D keypoint estimators, pre-trained on large-scale in-the-wild data, to obtain 3D joint positions. Next, we design a regression-based inverse-kinematic skeletal transformer that maps the joint positions to pose and shape representations from heavily noisy observations.
  arXiv  Detail & Related papers  (2024-04-19T04:51:18Z)
• Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives [70.32817882783608]
  We present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. Unlike existing primitive decomposition methods that rely on 3D input data, our approach operates directly on images. We show that the resulting textured primitives faithfully reconstruct the input images and accurately model the visible 3D points.
  arXiv  Detail & Related papers  (2023-07-11T17:58:31Z)
• Neural Capture of Animatable 3D Human from Monocular Video [38.974181971541846]
  We present a novel paradigm of building an animatable 3D human representation from a monocular video input, such that it can be rendered in any unseen poses and views. Our method is based on a dynamic Neural Radiance Field (NeRF) rigged by a mesh-based parametric 3D human model serving as a geometry proxy.
  arXiv  Detail & Related papers  (2022-08-18T09:20:48Z)
• Learning Multi-Object Dynamics with Compositional Neural Radiance Fields [63.424469458529906]
  We present a method to learn compositional predictive models from image observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and graph neural networks. NeRFs have become a popular choice for representing scenes due to their strong 3D prior. For planning, we utilize RRTs in the learned latent space, where we can exploit our model and the implicit object encoder to make sampling the latent space informative and more efficient.
  arXiv  Detail & Related papers  (2022-02-24T01:31:29Z)
• Learning Skeletal Articulations with Neural Blend Shapes [57.879030623284216]
  We develop a neural technique for articulating 3D characters using enveloping with a pre-defined skeletal structure. Our framework learns to rig and skin characters with the same articulation structure. We propose neural blend shapes which improve the deformation quality in the joint regions.
  arXiv  Detail & Related papers  (2021-05-06T05:58:13Z)
• A-NeRF: Surface-free Human 3D Pose Refinement via Neural Rendering [13.219688351773422]
  We propose a test-time optimization approach for monocular motion capture that learns a volumetric body model of the user in a self-supervised manner. Our approach is self-supervised and does not require any additional ground truth labels for appearance, pose, or 3D shape. We demonstrate that our novel combination of a discriminative pose estimation technique with surface-free analysis-by-synthesis outperforms purely discriminative monocular pose estimation approaches.
  arXiv  Detail & Related papers  (2021-02-11T18:58:31Z)
• Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency [114.02182755620784]
  We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision. Our framework is shown to outperform the state-of-the-art depth and motion estimation methods.
  arXiv  Detail & Related papers  (2021-02-04T14:26:42Z)
• TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style [43.99803542307155]
  We present TailorNet, a neural model which predicts clothing deformation in 3D as a function of three factors: pose, shape and style. Our hypothesis is that (even non-linear) combinations of examples smooth out high frequency components such as fine-wrinkles. Several experiments demonstrate TailorNet produces more realistic results than prior work, and even generates temporally coherent deformations.
  arXiv  Detail & Related papers  (2020-03-10T08:49:51Z)

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.