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Deformable 3D Gaussian Splatting for Animatable Human Avatars

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arxiv 2312.15059 v1 pith:XWFPBPV3 submitted 2023-12-22 cs.CV cs.AI

HyunJun Jung , Nikolas Brasch , Jifei Song , Eduardo Perez-Pellitero , Yiren Zhou , Zhihao Li , Nassir Navab , Benjamin Busam This is my paper
classification cs.CVcs.AI
keywords humanimagespardy-humanavataravatarsdynamicgaussianadditional
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent advances in neural radiance fields enable novel view synthesis of photo-realistic images in dynamic settings, which can be applied to scenarios with human animation. Commonly used implicit backbones to establish accurate models, however, require many input views and additional annotations such as human masks, UV maps and depth maps. In this work, we propose ParDy-Human (Parameterized Dynamic Human Avatar), a fully explicit approach to construct a digital avatar from as little as a single monocular sequence. ParDy-Human introduces parameter-driven dynamics into 3D Gaussian Splatting where 3D Gaussians are deformed by a human pose model to animate the avatar. Our method is composed of two parts: A first module that deforms canonical 3D Gaussians according to SMPL vertices and a consecutive module that further takes their designed joint encodings and predicts per Gaussian deformations to deal with dynamics beyond SMPL vertex deformations. Images are then synthesized by a rasterizer. 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 masks and can be trained with variable backgrounds while inferring full-resolution images efficiently even on consumer hardware. We provide experimental evidence to show that ParDy-Human outperforms state-of-the-art methods on ZJU-MoCap and THUman4.0 datasets both quantitatively and visually.

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Cited by 9 Pith papers

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

  1. Realizing Immersive Volumetric Video: A Multimodal Framework for 6-DoF VR Engagement

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    The paper presents a multimodal framework, dataset, and reconstruction pipeline to create immersive volumetric videos supporting large 6-DoF audiovisual interaction from real multi-view captures.

  2. ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction

    cs.GR 2025-09 reject novelty 7.0 of 10

    ContraGS trains 3D Gaussian Splatting directly on codebook-compressed representations, cutting peak model memory ~3.5x with small quality loss.

  3. 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.

  4. HOIGS: Human-Object Interaction Gaussian Splatting

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    HOIGS adds a cross-attention HOI module to Gaussian Splatting that combines HexPlane human features with Cubic Hermite Spline object features to model interaction-induced deformations.

  5. Splatography: Sparse multi-view dynamic Gaussian Splatting for filmmaking challenges

    cs.CV 2025-11 unverdicted novelty 6.0 of 10

    Splatography improves dynamic 3D reconstruction from sparse multi-view videos by splitting foreground and background Gaussian representations and applying tailored deformation learning for each.

  6. Restage4D: Reanimating Deformable 3D Reconstruction from a Single Video

    cs.CV 2025-08 conditional novelty 6.0 of 10

    Video-rewinding joint training preserves geometry while re-animating a single-video scene with novel motion from a text prompt and an image-to-video model.

  7. ArtGS:3D Gaussian Splatting for Interactive Visual-Physical Modeling and Manipulation of Articulated Objects

    cs.RO 2025-07 conditional novelty 6.0 of 10

    ArtGS combines multi-view 3D reconstruction, language-model joint initialization, and closed-loop optimization to improve articulated object manipulation.

  8. LUNA: Learning Universal 3D Human Animation Beyond Skinning

    cs.CV 2026-06 unverdicted novelty 4.0 of 10

    LUNA is an LBS-free neural animation model that maps 2D controls to 3D Gaussian deformations via a transformer motion regressor and hybrid supervision for realistic motion and zero-shot generalization.

  9. A Survey on 3D Gaussian Splatting

    cs.CV 2024-01 unverdicted novelty 2.0 of 10

    A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.

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