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Human101: Training 100+FPS Human Gaussians in 100s from 1 View

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arxiv 2312.15258 v1 pith:RYT5RQW6 submitted 2023-12-23 cs.CV

Mingwei Li , Jiachen Tao , Zongxin Yang , Yi Yang This is my paper · ORCID
classification cs.CV
keywords renderinghuman101gaussianshumangaussianhigh-fidelityhumansimages
verification ladder T0 review T1 audit T2 compute T3 formal
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Reconstructing the human body from single-view videos plays a pivotal role in the virtual reality domain. One prevalent application scenario necessitates the rapid reconstruction of high-fidelity 3D digital humans while simultaneously ensuring real-time rendering and interaction. Existing methods often struggle to fulfill both requirements. In this paper, we introduce Human101, a novel framework adept at producing high-fidelity dynamic 3D human reconstructions from 1-view videos by training 3D Gaussians in 100 seconds and rendering in 100+ FPS. Our method leverages the strengths of 3D Gaussian Splatting, which provides an explicit and efficient representation of 3D humans. Standing apart from prior NeRF-based pipelines, Human101 ingeniously applies a Human-centric Forward Gaussian Animation method to deform the parameters of 3D Gaussians, thereby enhancing rendering speed (i.e., rendering 1024-resolution images at an impressive 60+ FPS and rendering 512-resolution images at 100+ FPS). Experimental results indicate that our approach substantially eclipses current methods, clocking up to a 10 times surge in frames per second and delivering comparable or superior rendering quality. Code and demos will be released at https://github.com/longxiang-ai/Human101.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. 4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A diffusion model trained on 60,000 fitted 4D Gaussian Splatting human clips generates text-prompted, view-consistent dynamic humans directly in 4D, over 10x faster than video-first pipelines.

  2. 3D$^2$-Actor: Learning Pose-Conditioned 3D-Aware Denoiser for Realistic Gaussian Avatar Modeling

    cs.CV 2024-12 conditional novelty 6.0 of 10

    3D2-Actor interleaves pose-conditioned 2D denoising with 3D Gaussian rectification to generate realistic, temporally consistent human avatars from multi-view video.

  3. DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh

    cs.CV 2024-11 conditional novelty 6.0 of 10

    DAGSM is a text-to-3D avatar pipeline that generates body and garments as separate mesh-bound 2DGS models, enabling clothing replacement, texture editing, and animatable cloth.

  4. Sequential Gaussian Avatars with Hierarchical Motion Context

    cs.CV 2024-11 conditional novelty 5.0 of 10

    A 3D Gaussian avatar model that conditions non-rigid deformation on hierarchical skeleton and vertex motion reaches state-of-the-art rendering quality on three human-capture datasets.

  5. Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects

    cs.CV 2024-12 conditional novelty 4.0 of 10

    3D Gaussian Splatting research relevant to Extended Reality is organized into a five-part taxonomy with suggested future directions.

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