Physics-aware Hand-object Interaction Denoising

• URL: http://arxiv.org/abs/2405.11481v1
• Date: Sun, 19 May 2024 08:24:34 GMT
• Title: Physics-aware Hand-object Interaction Denoising
• Authors: Haowen Luo, Yunze Liu, Li Yi,
• Abstract summary: We introduce two learned loss terms that explicitly capture two crucial aspects of physical plausibility. These terms are used to train a physically-aware de-noising network. Our approach significantly improves both fine-grained physical plausibility and overall pose accuracy, surpassing current state-of-the-art de-noising methods.
• Score: 21.3448097349759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The credibility and practicality of a reconstructed hand-object interaction sequence depend largely on its physical plausibility. However, due to high occlusions during hand-object interaction, physical plausibility remains a challenging criterion for purely vision-based tracking methods. To address this issue and enhance the results of existing hand trackers, this paper proposes a novel physically-aware hand motion de-noising method. Specifically, we introduce two learned loss terms that explicitly capture two crucial aspects of physical plausibility: grasp credibility and manipulation feasibility. These terms are used to train a physically-aware de-noising network. Qualitative and quantitative experiments demonstrate that our approach significantly improves both fine-grained physical plausibility and overall pose accuracy, surpassing current state-of-the-art de-noising methods.

Related papers

• MultiPhys: Multi-Person Physics-aware 3D Motion Estimation [28.91813849219037]
  We introduce MultiPhys, a method designed for recovering multi-person motion from monocular videos. Our focus lies in capturing coherent spatial placement between pairs of individuals across varying degrees of engagement. We devise a pipeline in which the motion estimated by a kinematic-based method is fed into a physics simulator in an autoregressive manner.
  arXiv  Detail & Related papers  (2024-04-18T08:29:29Z)
• On Inherent Adversarial Robustness of Active Vision Systems [7.803487547944363]
  We show that two active vision methods - GFNet and FALcon - achieve (2-3) times greater robustness compared to a standard passive convolutional network under state-of-the-art adversarial attacks. More importantly, we provide illustrative and interpretable visualization analysis that demonstrates how performing inference from distinct fixation points makes active vision methods less vulnerable to malicious inputs.
  arXiv  Detail & Related papers  (2024-03-29T22:51:45Z)
• Object-Centric Conformance Alignments with Synchronization (Extended Version) [57.76661079749309]
  We present a new formalism that combines the ability of object-centric Petri nets to capture one-to-many relations and the one of Petri nets with identifiers to compare and synchronize objects based on their identity. We propose a conformance checking approach for such nets based on an encoding in satisfiability modulo theories (SMT)
  arXiv  Detail & Related papers  (2023-12-13T21:53:32Z)
• DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation [81.11585774044848]
  We present DeepSimHO, a novel deep-learning pipeline that combines forward physics simulation and backward gradient approximation with a neural network. Our method noticeably improves the stability of the estimation and achieves superior efficiency over test-time optimization.
  arXiv  Detail & Related papers  (2023-10-11T05:34:36Z)
• Fairness Increases Adversarial Vulnerability [50.90773979394264]
  This paper shows the existence of a dichotomy between fairness and robustness, and analyzes when achieving fairness decreases the model robustness to adversarial samples. Experiments on non-linear models and different architectures validate the theoretical findings in multiple vision domains. The paper proposes a simple, yet effective, solution to construct models achieving good tradeoffs between fairness and robustness.
  arXiv  Detail & Related papers  (2022-11-21T19:55:35Z)
• Physical Interaction: Reconstructing Hand-object Interactions with Physics [17.90852804328213]
  The paper proposes a physics-based method to better solve the ambiguities in the reconstruction. It first proposes a force-based dynamic model of the in-hand object, which recovers the unobserved contacts and also solves for plausible contact forces. Experiments show that the proposed technique reconstructs both physically plausible and more accurate hand-object interaction.
  arXiv  Detail & Related papers  (2022-09-22T07:41:31Z)
• Dynamic treatment effects: high-dimensional inference under model misspecification [11.688030627514532]
  Estimating dynamic treatment effects is essential across various disciplines. This paper introduces a new approach by proposing novel, robust estimators for both treatment assignments and outcome models.
  arXiv  Detail & Related papers  (2021-11-12T17:05:47Z)
• Adversarial Motion Modelling helps Semi-supervised Hand Pose Estimation [116.07661813869196]
  We propose to combine ideas from adversarial training and motion modelling to tap into unlabeled videos. We show that an adversarial leads to better properties of the hand pose estimator via semi-supervised training on unlabeled video sequences. The main advantage of our approach is that we can make use of unpaired videos and joint sequence data both of which are much easier to attain than paired training data.
  arXiv  Detail & Related papers  (2021-06-10T17:50:19Z)
• High-Robustness, Low-Transferability Fingerprinting of Neural Networks [78.2527498858308]
  This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks. It features high-robustness to the base model against model pruning as well as low-transferability to unassociated models.
  arXiv  Detail & Related papers  (2021-05-14T21:48:23Z)
• Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition [133.35968094967626]
  Skeleton-based action recognition has attracted increasing attention due to its strong adaptability to dynamic circumstances. With the help of deep learning techniques, it has also witnessed substantial progress and currently achieved around 90% accuracy in benign environment. Research on the vulnerability of skeleton-based action recognition under different adversarial settings remains scant.
  arXiv  Detail & Related papers  (2020-05-14T17:12:52Z)

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.