MASS: Mesh-inellipse Aligned Deformable Surfel Splatting for Hand Reconstruction and Rendering from Egocentric Monocular Video

• URL: http://arxiv.org/abs/2604.08943v1
• Date: Fri, 10 Apr 2026 04:22:48 GMT
• Title: MASS: Mesh-inellipse Aligned Deformable Surfel Splatting for Hand Reconstruction and Rendering from Egocentric Monocular Video
• Abstract summary: Reconstructing high-fidelity 3D hands from egocentric monocular videos remains a challenge.<n>Existing methods often incur high computational costs, making them impractical for real-time applications.<n>We propose Mesh-inellipse Aligned deformable Surfel Splatting to address these challenges.
• Score: 19.581474149435092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing high-fidelity 3D hands from egocentric monocular videos remains a challenge due to the limitations in capturing high-resolution geometry, hand-object interactions, and complex objects on hands. Additionally, existing methods often incur high computational costs, making them impractical for real-time applications. In this work, we propose Mesh-inellipse Aligned deformable Surfel Splatting (MASS) to address these challenges by leveraging a deformable 2D Gaussian Surfel representation. We introduce the mesh-aligned Steiner Inellipse and fractal densification for mesh-to-surfel conversion that initiates high-resolution 2D Gaussian surfels from coarse parametric hand meshes, providing surface representation with photorealistic rendering potential. Second, we propose Gaussian Surfel Deformation, which enables efficient modeling of hand deformations and personalized features by predicting residual updates to surfel attributes and introducing an opacity mask to refine geometry and texture without adaptive density control. In addition, we propose a two-stage training strategy and a novel binding loss to improve the optimization robustness and reconstruction quality. Extensive experiments on the ARCTIC dataset, the Hand Appearance dataset, and the Interhand2.6M dataset demonstrate that our model achieves superior reconstruction performance compared to state-of-the-art methods.

Related papers

• Surfsvr: 2D Surface Priors as 3D Geometric Regularizers for Sparse Voxel Reconstruction [56.51413081953813]
  SurfSVR is a novel sparse voxel reconstruction paradigm that treats 2D surface priors as explicit 3D geometric regularizers.<n>It consistently improves sparse voxel reconstruction across scenes with substantially different visibility and geometry characteristics.
  arXiv  Detail & Related papers  (2026-08-12T11:25:31Z)
• VEPHand: View-Efficient Photometric Hand Performance Capture at Scale [27.713586141581498]
  This paper presents an end-to-end pipeline for dynamic hand performance capture and registration.<n>Our method achieves state-of-the-art reconstruction fidelity in view-efficient, unmasked scenarios and highly accurate registration.
  arXiv  Detail & Related papers  (2026-06-14T18:47:44Z)
• GauSSmart: Enhanced 3D Reconstruction through 2D Foundation Models and Geometric Filtering [50.675710727721786]
  We propose GauSSmart, a hybrid method that bridges 2D foundational models and 3D Gaussian Splatting reconstruction.<n>Our approach integrates established 2D computer vision techniques, including convex filtering and semantic feature supervision.<n>We validate our approach across three datasets, where GauSSmart consistently outperforms existing Gaussian Splatting.
  arXiv  Detail & Related papers  (2025-10-16T03:38:26Z)
• 3D Gaussian Flats: Hybrid 2D/3D Photometric Scene Reconstruction [62.84879632157956]
  We propose a novel hybrid 2D/3D representation that jointly optimize constrained planar (2D) Gaussians for modeling flat surfaces and freeform (3D) Gaussians for the rest of the scene.<n>Our end-to-end approach dynamically detects and refines planar regions, improving both visual fidelity and geometric accuracy.<n>It achieves state-of-the-art depth estimation on ScanNet++ and ScanNetv2, and excels at mesh extraction without overfitting to a specific camera model.
  arXiv  Detail & Related papers  (2025-09-19T21:04:36Z)
• FMGS-Avatar: Mesh-Guided 2D Gaussian Splatting with Foundation Model Priors for 3D Monocular Avatar Reconstruction [18.570290675633732]
  We introduce Mesh-Guided 2D Gaussian Splatting, where 2D primitives are attached directly to template mesh faces with constrained position, rotation, and movement.<n>We leverage foundation models trained on large-scale datasets, such as Sapiens, to complement the limited visual cues from monocular videos.<n> Experimental evaluation demonstrates superior reconstruction quality compared to existing methods, with notable gains in geometric accuracy and appearance fidelity.
  arXiv  Detail & Related papers  (2025-09-18T08:41:41Z)
• A Controllable 3D Deepfake Generation Framework with Gaussian Splatting [6.969908558294805]
  We propose a novel 3D deepfake generation framework based on 3D Gaussian Splatting.<n>It enables realistic, identity-preserving face swapping and reenactment in a fully controllable 3D space.<n>Our approach bridges the gap between 3D modeling and deepfake synthesis, enabling new directions for scene-aware, controllable, and immersive visual forgeries.
  arXiv  Detail & Related papers  (2025-09-15T06:34:17Z)
• SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling [79.56581753856452]
  SparseFlex is a novel sparse-structured isosurface representation that enables differentiable mesh reconstruction at resolutions up to $10243$ directly from rendering losses.<n>By enabling high-resolution, differentiable mesh reconstruction and generation with rendering losses, SparseFlex significantly advances the state-of-the-art in 3D shape representation and modeling.
  arXiv  Detail & Related papers  (2025-03-27T17:46:42Z)
• 3D Gaussian Splatting with Normal Information for Mesh Extraction and Improved Rendering [8.59572577251833]
  We propose a novel regularization method using the gradients of a signed distance function estimated from the Gaussians.<n>We demonstrate the effectiveness of our approach on datasets such as Mip-NeRF360, Tanks and Temples, and Deep-Blending.
  arXiv  Detail & Related papers  (2025-01-14T18:40:33Z)
• Hybrid Explicit Representation for Ultra-Realistic Head Avatars [55.829497543262214]
  We introduce a novel approach to creating ultra-realistic head avatars and rendering them in real-time.<n> UV-mapped 3D mesh is utilized to capture sharp and rich textures on smooth surfaces, while 3D Gaussian Splatting is employed to represent complex geometric structures.<n>Experiments that our modeled results exceed those of state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-03-18T04:01:26Z)
• Flexible Isosurface Extraction for Gradient-Based Mesh Optimization [65.76362454554754]
  This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field. We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives.
  arXiv  Detail & Related papers  (2023-08-10T06:40:19Z)

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.