GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh
We introduce GoMAvatar, a novel approach for real-time, memory-efficient, high-quality animatable human modeling. GoMAvatar takes as input a single monocular video to create a digital avatar capable of re-articulation in new poses and real-time rendering from novel viewpoints, while seamlessly integrating with rasterization-based graphics pipelines. Central to our method is the Gaussians-on-Mesh representation, a hybrid 3D model combining rendering quality and speed of Gaussian splatting with geometry modeling and compatibility of deformable meshes. We assess GoMAvatar on ZJU-MoCap data and various YouTube videos. GoMAvatar matches or surpasses current monocular human modeling algorithms in rendering quality and significantly outperforms them in computational efficiency (43 FPS) while being memory-efficient (3.63 MB per subject).
Code (1)
Tasks
Computational EfficiencyMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Deblur-Avatar: Animatable Avatars from Motion-Blurred Monocular Videos
We introduce Deblur-Avatar, a novel framework for modeling high-fidelity, animatable 3D human avatars from motion-blurred monocular video inputs. Motion blur is prevalent in real-world dynamic video capture, especially d…
3DGSGaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians
We present GaussianAvatar, an efficient approach to creating realistic human avatars with dynamic 3D appearances from a single video. We start by introducing animatable 3D Gaussians to explicitly represent humans in vari…
Motion EstimationiHuman: Instant Animatable Digital Humans From Monocular Videos
Personalized 3D avatars require an animatable representation of digital humans. Doing so instantly from monocular videos offers scalability to broad class of users and wide-scale applications. In this paper, we present a…
3D geometry3D ReconstructionInstantGeoAvatar: Effective Geometry and Appearance Modeling of Animatable Avatars from Monocular Video
We present InstantGeoAvatar, a method for efficient and effective learning from monocular video of detailed 3D geometry and appearance of animatable implicit human avatars. Our key observation is that the optimization of…
3D geometryNovel View Synthesis2DGS-Avatar: Animatable High-fidelity Clothed Avatar via 2D Gaussian Splatting
Real-time rendering of high-fidelity and animatable avatars from monocular videos remains a challenging problem in computer vision and graphics. Over the past few years, the Neural Radiance Field (NeRF) has made signific…
3DGSNeRF