GaussianBody: Clothed Human Reconstruction via 3d Gaussian Splatting
- URL: http://arxiv.org/abs/2401.09720v2
- Date: Sat, 27 Jan 2024 06:54:18 GMT
- Title: GaussianBody: Clothed Human Reconstruction via 3d Gaussian Splatting
- Authors: Mengtian Li, Shengxiang Yao, Zhifeng Xie, Keyu Chen
- Abstract summary: 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.
- Score: 14.937297984020821
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this work, we propose a novel clothed human reconstruction method called
GaussianBody, based on 3D Gaussian Splatting. Compared with the costly neural
radiance based models, 3D Gaussian Splatting has recently demonstrated great
performance in terms of training time and rendering quality. However, 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. To address these challenges, our method considers explicit pose-guided
deformation to associate dynamic Gaussians across the canonical space and the
observation space, introducing a physically-based prior with regularized
transformations helps mitigate ambiguity between the two spaces. During the
training process, we further propose a pose refinement strategy to update the
pose regression for compensating the inaccurate initial estimation and a
split-with-scale mechanism to enhance the density of regressed point clouds.
The experiments validate that our method can achieve state-of-the-art
photorealistic novel-view rendering results with high-quality details for
dynamic clothed human bodies, along with explicit geometry reconstruction.
Related papers
- 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) - USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting [45.246178004823534]
Spike cameras, as an innovative neuromorphic camera that captures scenes with the 0-1 bit stream at 40 kHz, are increasingly employed for the 3D reconstruction task.
Previous spike-based 3D reconstruction approaches often employ a casecased pipeline.
We propose a synergistic optimization framework, textbfUSP-Gaussian, that unifies spike-based image reconstruction, pose correction, and Gaussian splatting into an end-to-end framework.
arXiv Detail & Related papers (2024-11-15T14:15:16Z) - 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) - No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images [100.80376573969045]
NoPoSplat is a feed-forward model capable of reconstructing 3D scenes parameterized by 3D Gaussians from multi-view images.
Our model achieves real-time 3D Gaussian reconstruction during inference.
This work makes significant advances in pose-free generalizable 3D reconstruction and demonstrates its applicability to real-world scenarios.
arXiv Detail & Related papers (2024-10-31T17:58:22Z) - PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices.
Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
arXiv Detail & Related papers (2024-10-29T15:28:15Z) - Generalizable Human Gaussians for Sparse View Synthesis [48.47812125126829]
This paper introduces a new method to learn generalizable human Gaussians that allows photorealistic and accurate view-rendering of a new human subject from a limited set of sparse views.
A pivotal innovation of our approach involves reformulating the learning of 3D Gaussian parameters into a regression process defined on the 2D UV space of a human template.
Our method outperforms recent methods on both within-dataset generalization as well as cross-dataset generalization settings.
arXiv Detail & Related papers (2024-07-17T17:56:30Z) - Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes [50.92217884840301]
Gaussian Opacity Fields (GOF) is a novel approach for efficient, high-quality, and adaptive surface reconstruction in scenes.
GOF is derived from ray-tracing-based volume rendering of 3D Gaussians.
GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis.
arXiv Detail & Related papers (2024-04-16T17:57:19Z) - Mesh-based Gaussian Splatting for Real-time Large-scale Deformation [58.18290393082119]
It is challenging for users to directly deform or manipulate implicit representations with large deformations in the real-time fashion.
We develop a novel GS-based method that enables interactive deformation.
Our approach achieves high-quality reconstruction and effective deformation, while maintaining the promising rendering results at a high frame rate.
arXiv Detail & Related papers (2024-02-07T12:36:54Z) - Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction [8.260048622127913]
Reconstructing dynamic objects from monocular videos is a severely underconstrained and challenging problem.
We introduce Neural Parametric Gaussians (NPGs) to take on this challenge by imposing a two-stage approach.
NPGs achieve superior results compared to previous works, especially in challenging scenarios with few multi-view cues.
arXiv Detail & Related papers (2023-12-02T18:06:24Z)
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.