SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data

• URL: http://arxiv.org/abs/2007.15506v1
• Date: Thu, 30 Jul 2020 14:59:38 GMT
• Title: SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data
• Authors: Tyler Zhu, Per Karlsson, Christoph Bregler
• Abstract summary: We present a technique for learning difficult per-pixel 2.5D and 3D regression representations of articulated people. We obtained strong sim-to-real domain generalization for the 2.5DPose estimation task and the 3D human surface normal estimation task. Our approach is complementary to existing domain-adaptation techniques and can be applied to other dense per-pixel pose estimation problems.
• Score: 7.053519629075887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With a proliferation of generic domain-adaptation approaches, we report a simple yet effective technique for learning difficult per-pixel 2.5D and 3D regression representations of articulated people. We obtained strong sim-to-real domain generalization for the 2.5D DensePose estimation task and the 3D human surface normal estimation task. On the multi-person DensePose MSCOCO benchmark, our approach outperforms the state-of-the-art methods which are trained on real images that are densely labelled. This is an important result since obtaining human manifold's intrinsic uv coordinates on real images is time consuming and prone to labeling noise. Additionally, we present our model's 3D surface normal predictions on the MSCOCO dataset that lacks any real 3D surface normal labels. The key to our approach is to mitigate the "Inter-domain Covariate Shift" with a carefully selected training batch from a mixture of domain samples, a deep batch-normalized residual network, and a modified multi-task learning objective. Our approach is complementary to existing domain-adaptation techniques and can be applied to other dense per-pixel pose estimation problems.

Related papers

• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• 3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models [52.96248836582542]
  We propose an effective approach based on recent diffusion models, termed HumanWild, which can effortlessly generate human images and corresponding 3D mesh annotations. By exclusively employing generative models, we generate large-scale in-the-wild human images and high-quality annotations, eliminating the need for real-world data collection.
  arXiv  Detail & Related papers  (2024-03-17T06:31:16Z)
• Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation [18.011044932979143]
  3DUDA is a method capable of adapting to a nuisance-ridden target domain without 3D or depth data. We represent object categories as simple cuboid meshes, and harness a generative model of neural feature activations. We show that our method simulates fine-tuning on a global pseudo-labeled dataset under mild assumptions.
  arXiv  Detail & Related papers  (2024-01-19T17:48:05Z)
• GS-Pose: Category-Level Object Pose Estimation via Geometric and Semantic Correspondence [5.500735640045456]
  Category-level pose estimation is a challenging task with many potential applications in computer vision and robotics. We propose to utilize both geometric and semantic features obtained from a pre-trained foundation model. This requires significantly less data to train than prior methods since the semantic features are robust to object texture and appearance.
  arXiv  Detail & Related papers  (2023-11-23T02:35:38Z)
• Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation [29.037799937729687]
  Learning-based methods have dominated the 3D human pose estimation (HPE) tasks with significantly better performance in most benchmarks than traditional optimization-based methods. We propose textbfZero-shot textbfDiffusion-based textbfOptimization (textbfZeDO) pipeline for 3D HPE. Our multi-hypothesis textittextbfZeDO achieves state-of-the-art (SOTA) performance on Human3.6M, with minMPJPE $51.4$
  arXiv  Detail & Related papers  (2023-07-07T21:03:18Z)
• Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning [70.75369367311897]
  3D-aware global correspondences are reliable flows that jointly encode global semantic correlations, local deformations, and geometric priors of 3D human bodies. An adversarial generator takes the garment warped by the 3D-aware flow, and the image of the target person as inputs, to synthesize the photo-realistic try-on result.
  arXiv  Detail & Related papers  (2022-11-25T12:16:21Z)
• Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics [6.516912796655748]
  This paper presents a technique for estimating the normal from 3D point clouds and 2D colour images. We have developed a transformer neural network that learns to utilise the hybrid information of visual semantic and 3D geometric data.
  arXiv  Detail & Related papers  (2022-11-19T03:55:09Z)
• Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images [79.70127290464514]
  We decompose the task into two stages, i.e. person localization and pose estimation. And we propose three task-specific graph neural networks for effective message passing. Our approach achieves state-of-the-art performance on CMU Panoptic and Shelf datasets.
  arXiv  Detail & Related papers  (2021-09-13T11:44:07Z)
• Adapted Human Pose: Monocular 3D Human Pose Estimation with Zero Real 3D Pose Data [14.719976311208502]
  Training vs. test data domain gaps often negatively affect model performance. We present our adapted human pose (AHuP) approach that addresses adaptation problems in both appearance and pose spaces. AHuP is built around a practical assumption that in real applications, data from target domain could be inaccessible or only limited information can be acquired.
  arXiv  Detail & Related papers  (2021-05-23T01:20:40Z)
• 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)
• Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry [62.29762409558553]
  Epipolar constraints are at the core of feature matching and depth estimation in multi-person 3D human pose estimation methods. Despite the satisfactory performance of this formulation in sparser crowd scenes, its effectiveness is frequently challenged under denser crowd circumstances. In this paper, we depart from the multi-person 3D pose estimation formulation, and instead reformulate it as crowd pose estimation.
  arXiv  Detail & Related papers  (2020-07-21T17:59:36Z)

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.