BIGS: Bimanual Category-agnostic Interaction Reconstruction from Monocular Videos via 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2504.09097v1
• Date: Sat, 12 Apr 2025 06:30:24 GMT
• Title: BIGS: Bimanual Category-agnostic Interaction Reconstruction from Monocular Videos via 3D Gaussian Splatting
• Authors: Jeongwan On, Kyeonghwan Gwak, Gunyoung Kang, Junuk Cha, Soohyun Hwang, Hyein Hwang, Seungryul Baek,
• Abstract summary: We introduce BIGS (Bimanual Interaction 3D Gaussian Splatting), a method that reconstructs 3D Gaussians of hands and an unknown object from a monocular video.<n>Our method achieves the state-of-the-art accuracy on two challenging datasets, in terms of 3D hand pose estimation (MPJPE), 3D object reconstruction (CDh, CDo, F10), and rendering quality (PSNR, SSIM, LPIPS, respectively)
• Score: 3.905416830166856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing 3Ds of hand-object interaction (HOI) is a fundamental problem that can find numerous applications. Despite recent advances, there is no comprehensive pipeline yet for bimanual class-agnostic interaction reconstruction from a monocular RGB video, where two hands and an unknown object are interacting with each other. Previous works tackled the limited hand-object interaction case, where object templates are pre-known or only one hand is involved in the interaction. The bimanual interaction reconstruction exhibits severe occlusions introduced by complex interactions between two hands and an object. To solve this, we first introduce BIGS (Bimanual Interaction 3D Gaussian Splatting), a method that reconstructs 3D Gaussians of hands and an unknown object from a monocular video. To robustly obtain object Gaussians avoiding severe occlusions, we leverage prior knowledge of pre-trained diffusion model with score distillation sampling (SDS) loss, to reconstruct unseen object parts. For hand Gaussians, we exploit the 3D priors of hand model (i.e., MANO) and share a single Gaussian for two hands to effectively accumulate hand 3D information, given limited views. To further consider the 3D alignment between hands and objects, we include the interacting-subjects optimization step during Gaussian optimization. Our method achieves the state-of-the-art accuracy on two challenging datasets, in terms of 3D hand pose estimation (MPJPE), 3D object reconstruction (CDh, CDo, F10), and rendering quality (PSNR, SSIM, LPIPS), respectively.

Related papers

• HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based Data Augmentation [29.766317710266765]
  We propose a new 3D Gaussian Splatting based data augmentation framework for bimanual hand-object interaction.<n>We use mesh-based 3DGS to model objects and hands, and to deal with the rendering blur problem due to multi-resolution input images used.<n>We extend the single hand grasping pose optimization module for the bimanual hand object to generate various poses of bimanual hand-object interaction.
  arXiv  Detail & Related papers  (2025-01-06T08:48:17Z)
• HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video [70.11702620562889]
  HOLD -- the first category-agnostic method that reconstructs an articulated hand and object jointly from a monocular interaction video. We develop a compositional articulated implicit model that can disentangled 3D hand and object from 2D images. Our method does not rely on 3D hand-object annotations while outperforming fully-supervised baselines in both in-the-lab and challenging in-the-wild settings.
  arXiv  Detail & Related papers  (2023-11-30T10:50:35Z)
• Decaf: Monocular Deformation Capture for Face and Hand Interactions [77.75726740605748]
  This paper introduces the first method that allows tracking human hands interacting with human faces in 3D from single monocular RGB videos. We model hands as articulated objects inducing non-rigid face deformations during an active interaction. Our method relies on a new hand-face motion and interaction capture dataset with realistic face deformations acquired with a markerless multi-view camera system.
  arXiv  Detail & Related papers  (2023-09-28T17:59:51Z)
• SHOWMe: Benchmarking Object-agnostic Hand-Object 3D Reconstruction [13.417086460511696]
  We introduce the SHOWMe dataset which consists of 96 videos, annotated with real and detailed hand-object 3D textured meshes. We consider a rigid hand-object scenario, in which the pose of the hand with respect to the object remains constant during the whole video sequence. This assumption allows us to register sub-millimetre-precise groundtruth 3D scans to the image sequences in SHOWMe.
  arXiv  Detail & Related papers  (2023-09-19T16:48:29Z)
• Learning Explicit Contact for Implicit Reconstruction of Hand-held Objects from Monocular Images [59.49985837246644]
  We show how to model contacts in an explicit way to benefit the implicit reconstruction of hand-held objects. In the first part, we propose a new subtask of directly estimating 3D hand-object contacts from a single image. In the second part, we introduce a novel method to diffuse estimated contact states from the hand mesh surface to nearby 3D space.
  arXiv  Detail & Related papers  (2023-05-31T17:59:26Z)
• TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement [42.3418874174372]
  We present TOCH, a method for refining incorrect 3D hand-object interaction sequences using a data prior. We learn a latent manifold of plausible TOCH fields with a temporal denoising auto-encoder. Experiments demonstrate that TOCH outperforms state-of-the-art 3D hand-object interaction models.
  arXiv  Detail & Related papers  (2022-05-16T20:41:45Z)
• Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements [96.40125818594952]
  We make the first attempt to reconstruct 3D interacting hands from monocular single RGB images. Our method can generate 3D hand meshes with both precise 3D poses and minimal collisions.
  arXiv  Detail & Related papers  (2021-11-01T08:24:10Z)
• RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Video [76.86512780916827]
  We present the first real-time method for motion capture of skeletal pose and 3D surface geometry of hands from a single RGB camera. In order to address the inherent depth ambiguities in RGB data, we propose a novel multi-task CNN. We experimentally verify the individual components of our RGB two-hand tracking and 3D reconstruction pipeline.
  arXiv  Detail & Related papers  (2021-06-22T12:53:56Z)
• Reconstructing Hand-Object Interactions in the Wild [71.16013096764046]
  We propose an optimization-based procedure which does not require direct 3D supervision. We exploit all available related data (2D bounding boxes, 2D hand keypoints, 2D instance masks, 3D object models, 3D in-the-lab MoCap) to provide constraints for the 3D reconstruction. Our method produces compelling reconstructions on the challenging in-the-wild data from the EPIC Kitchens and the 100 Days of Hands datasets.
  arXiv  Detail & Related papers  (2020-12-17T18:59:58Z)

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.