Unsupervised Geodesic-preserved Generative Adversarial Networks for Unconstrained 3D Pose Transfer

• URL: http://arxiv.org/abs/2108.07520v1
• Date: Tue, 17 Aug 2021 09:08:21 GMT
• Title: Unsupervised Geodesic-preserved Generative Adversarial Networks for Unconstrained 3D Pose Transfer
• Authors: Haoyu Chen, Hao Tang, Henglin Shi, Wei Peng, Nicu Sebe, Guoying Zhao
• Abstract summary: We present an unsupervised approach to conduct the pose transfer between any arbitrated given 3D meshes. Specifically, a novel Intrinsic-Extrinsic Preserved Generative Adrative Network (IEP-GAN) is presented for both intrinsic (i.e., shape) and extrinsic (i.e., pose) information preservation. Our proposed model produces better results and is substantially more efficient compared to recent state-of-the-art methods.
• Score: 84.04540436494011
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the strength of deep generative models, 3D pose transfer regains intensive research interests in recent years. Existing methods mainly rely on a variety of constraints to achieve the pose transfer over 3D meshes, e.g., the need for the manually encoding for shape and pose disentanglement. In this paper, we present an unsupervised approach to conduct the pose transfer between any arbitrate given 3D meshes. Specifically, a novel Intrinsic-Extrinsic Preserved Generative Adversarial Network (IEP-GAN) is presented for both intrinsic (i.e., shape) and extrinsic (i.e., pose) information preservation. Extrinsically, we propose a co-occurrence discriminator to capture the structural/pose invariance from distinct Laplacians of the mesh. Meanwhile, intrinsically, a local intrinsic-preserved loss is introduced to preserve the geodesic priors while avoiding the heavy computations. At last, we show the possibility of using IEP-GAN to manipulate 3D human meshes in various ways, including pose transfer, identity swapping and pose interpolation with latent code vector arithmetic. The extensive experiments on various 3D datasets of humans, animals and hands qualitatively and quantitatively demonstrate the generality of our approach. Our proposed model produces better results and is substantially more efficient compared to recent state-of-the-art methods. Code is available: https://github.com/mikecheninoulu/Unsupervised_IEPGAN.

Related papers

• Graph and Skipped Transformer: Exploiting Spatial and Temporal Modeling Capacities for Efficient 3D Human Pose Estimation [36.93661496405653]
  We take a global approach to exploit Transformer-temporal information with a concise Graph and Skipped Transformer architecture. Specifically, in 3D pose stage, coarse-grained body parts are deployed to construct a fully data-driven adaptive model. Experiments are conducted on Human3.6M, MPI-INF-3DHP and Human-Eva benchmarks.
  arXiv  Detail & Related papers  (2024-07-03T10:42:09Z)
• GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image [94.56927147492738]
  We introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes from single images. We show that leveraging diffusion priors can markedly improve generalization, detail preservation, and efficiency in resource usage. We propose a simple yet effective strategy to segregate the complex data distribution of various scenes into distinct sub-distributions.
  arXiv  Detail & Related papers  (2024-03-18T17:50:41Z)
• Self-supervised Learning for Enhancing Geometrical Modeling in 3D-Aware Generative Adversarial Network [42.16520614686877]
  3D-GANs exhibit artifacts in their 3D geometrical modeling, such as mesh imperfections and holes. These shortcomings are primarily attributed to the limited availability of annotated 3D data. We present a Self-Supervised Learning technique tailored as an auxiliary loss for any 3D-GAN.
  arXiv  Detail & Related papers  (2023-12-19T04:55:33Z)
• MAPConNet: Self-supervised 3D Pose Transfer with Mesh and Point Contrastive Learning [32.97354536302333]
  3D pose transfer is a challenging generation task that aims to transfer the pose of a source geometry onto a target geometry with the target identity preserved. Current pose transfer methods allow end-to-end correspondence learning but require the desired final output as ground truth for supervision. We present a novel self-supervised framework for 3D pose transfer which can be trained in unsupervised, semi-supervised, or fully supervised settings.
  arXiv  Detail & Related papers  (2023-04-26T20:42:40Z)
• Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation [46.38290735670527]
  Recovering multi-person 3D poses from a single RGB image is a severely ill-conditioned problem. Recent works have shown promising results by simultaneously reasoning for different people but in all cases within a local neighborhood. PI-Net introduces a self-attention block to reason for all people in the image at the same time and refine potentially noisy initial 3D poses. In this paper, we model people interactions at a whole, independently of their number, and in a permutation-invariant manner building upon the Set Transformer.
  arXiv  Detail & Related papers  (2022-04-11T07:23:54Z)
• Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation [63.199549837604444]
  3D human pose estimation approaches leverage different forms of strong (2D/3D pose) or weak (multi-view or depth) paired supervision. We cast 3D pose learning as a self-supervised adaptation problem that aims to transfer the task knowledge from a labeled source domain to a completely unpaired target. We evaluate different self-adaptation settings and demonstrate state-of-the-art 3D human pose estimation performance on standard benchmarks.
  arXiv  Detail & Related papers  (2022-04-05T03:52:57Z)
• On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation [57.766049538913926]
  Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant. Much of the recent attention has shifted towards semi and (or) weakly supervised learning. We propose to impose multi-view geometrical constraints by means of a differentiable triangulation and to use it as form of self-supervision during training when no labels are available.
  arXiv  Detail & Related papers  (2022-03-29T19:11:54Z)
• Geometry-Contrastive Transformer for Generalized 3D Pose Transfer [95.56457218144983]
  The intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. We propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies. We present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task.
  arXiv  Detail & Related papers  (2021-12-14T13:14:24Z)
• Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses [74.48263583706712]
  3D human pose estimation from a single image is an inverse problem due to the inherent ambiguity of the missing depth. We propose a weakly supervised deep generative network to address the inverse problem.
  arXiv  Detail & Related papers  (2020-08-13T09:26:01Z)

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.