EfficientHuman: Efficient Training and Reconstruction of Moving Human using Articulated 2D Gaussian

• URL: http://arxiv.org/abs/2504.20607v1
• Date: Tue, 29 Apr 2025 10:15:43 GMT
• Title: EfficientHuman: Efficient Training and Reconstruction of Moving Human using Articulated 2D Gaussian
• Authors: Hao Tian, Rui Liu, Wen Shen, Yilong Hu, Zhihao Zheng, Xiaolin Qin,
• Abstract summary: Recent work on reconstructing the 3D human body using 3DGS attempts to leverage prior information on human pose to enhance rendering quality and improve training speed.<n>It struggles to effectively fit dynamic surface planes due to multi-view inconsistency and redundant Gaussians.<n>We propose EfficientHuman, a model that quickly accomplishes the dynamic reconstruction of the human body using Articulated 2D Gaussian.
• Score: 15.56606942574165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) has been recognized as a pioneering technique in scene reconstruction and novel view synthesis. Recent work on reconstructing the 3D human body using 3DGS attempts to leverage prior information on human pose to enhance rendering quality and improve training speed. However, it struggles to effectively fit dynamic surface planes due to multi-view inconsistency and redundant Gaussians. This inconsistency arises because Gaussian ellipsoids cannot accurately represent the surfaces of dynamic objects, which hinders the rapid reconstruction of the dynamic human body. Meanwhile, the prevalence of redundant Gaussians means that the training time of these works is still not ideal for quickly fitting a dynamic human body. To address these, we propose EfficientHuman, a model that quickly accomplishes the dynamic reconstruction of the human body using Articulated 2D Gaussian while ensuring high rendering quality. The key innovation involves encoding Gaussian splats as Articulated 2D Gaussian surfels in canonical space and then transforming them to pose space via Linear Blend Skinning (LBS) to achieve efficient pose transformations. Unlike 3D Gaussians, Articulated 2D Gaussian surfels can quickly conform to the dynamic human body while ensuring view-consistent geometries. Additionally, we introduce a pose calibration module and an LBS optimization module to achieve precise fitting of dynamic human poses, enhancing the model's performance. Extensive experiments on the ZJU-MoCap dataset demonstrate that EfficientHuman achieves rapid 3D dynamic human reconstruction in less than a minute on average, which is 20 seconds faster than the current state-of-the-art method, while also reducing the number of redundant Gaussians.

Related papers

• SIGMAN:Scaling 3D Human Gaussian Generation with Millions of Assets [72.26350984924129]
  We propose a latent space generation paradigm for 3D human digitization.<n>We transform the ill-posed low-to-high-dimensional mapping problem into a learnable distribution shift.<n>We employ the multi-view optimization approach combined with synthetic data to construct the HGS-1M dataset.
  arXiv  Detail & Related papers  (2025-04-09T15:38:18Z)
• DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes [71.61083731844282]
  We present DeSiRe-GS, a self-supervised gaussian splatting representation. It enables effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios.
  arXiv  Detail & Related papers  (2024-11-18T05:49:16Z)
• HFGaussian: Learning Generalizable Gaussian Human with Integrated Human Features [23.321087432786605]
  We present a novel approach called HFGaussian that can estimate novel views and human features, such as the 3D skeleton, 3D key points, and dense pose, from sparse input images in real time at 25 FPS. We thoroughly evaluate our HFGaussian method against the latest state-of-the-art techniques in human Gaussian splatting and pose estimation, demonstrating its real-time, state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-05T13:31:04Z)
• 2D Gaussian Splatting for Geometrically Accurate Radiance Fields [50.056790168812114]
  3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking.<n>We present 2D Gaussian Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate radiance fields from multi-view images.<n>We demonstrate that our differentiable terms allows for noise-free and detailed geometry reconstruction while maintaining competitive appearance quality, fast training speed, and real-time rendering.
  arXiv  Detail & Related papers  (2024-03-26T17:21:24Z)
• GaussianBody: Clothed Human Reconstruction via 3d Gaussian Splatting [14.937297984020821]
  We propose a novel clothed human reconstruction method called GaussianBody, based on 3D Gaussian Splatting. Applying the static 3D Gaussian Splatting model to the dynamic human reconstruction problem is non-trivial due to complicated non-rigid deformations and rich cloth details. We show that our method can achieve state-of-the-art photorealistic novel-view rendering results with high-quality details for dynamic clothed human bodies.
  arXiv  Detail & Related papers  (2024-01-18T04:48:13Z)
• Deformable 3D Gaussian Splatting for Animatable Human Avatars [50.61374254699761]
  We propose a fully explicit approach to construct a digital avatar from as little as a single monocular sequence. ParDy-Human constitutes an explicit model for realistic dynamic human avatars which requires significantly fewer training views and images. Our avatars learning is free of additional annotations such as Splat masks and can be trained with variable backgrounds while inferring full-resolution images efficiently even on consumer hardware.
  arXiv  Detail & Related papers  (2023-12-22T20:56:46Z)
• GauHuman: Articulated Gaussian Splatting from Monocular Human Videos [58.553979884950834]
  GauHuman is a 3D human model with Gaussian Splatting for both fast training (1 2 minutes) and real-time rendering (up to 189 FPS) GauHuman encodes Gaussian Splatting in the canonical space and transforms 3D Gaussians from canonical space to posed space with linear blend skinning (LBS) Experiments on ZJU_Mocap and MonoCap datasets demonstrate that GauHuman achieves state-of-the-art performance quantitatively and qualitatively with fast training and real-time rendering speed.
  arXiv  Detail & Related papers  (2023-12-05T18:59:14Z)
• HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting [113.37908093915837]
  Existing methods optimize 3D representations like mesh or neural fields via score distillation sampling (SDS), which suffers from inadequate fine details or excessive training time. In this paper, we propose an efficient yet effective framework, HumanGaussian, that generates high-quality 3D humans with fine-grained geometry and realistic appearance.
  arXiv  Detail & Related papers  (2023-11-28T18:59:58Z)
• Animatable 3D Gaussians for High-fidelity Synthesis of Human Motions [37.50707388577952]
  We present a novel animatable 3D Gaussian model for rendering high-fidelity free-view human motions in real time. Compared to existing NeRF-based methods, the model owns better capability in high-frequency details without the jittering problem across video frames.
  arXiv  Detail & Related papers  (2023-11-22T14:00:23Z)

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.