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Drivable 3D Gaussian Avatars

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arxiv 2311.08581 v2 pith:J4T2SD5M submitted 2023-11-14 cs.CV

Wojciech Zielonka , Timur Bagautdinov , Shunsuke Saito , Michael Zollhöfer , Justus Thies , Javier Romero This is my paper · ORCID
classification cs.CV
keywords gaussianprimitivestetrahedronavatarsbodycagesdeformationdrivable
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We present Drivable 3D Gaussian Avatars (D3GA), a multi-layered 3D controllable model for human bodies that utilizes 3D Gaussian primitives embedded into tetrahedral cages. The advantage of using cages compared to commonly employed linear blend skinning (LBS) is that primitives like 3D Gaussians are naturally re-oriented and their kernels are stretched via the deformation gradients of the encapsulating tetrahedron. Additional offsets are modeled for the tetrahedron vertices, effectively decoupling the low-dimensional driving poses from the extensive set of primitives to be rendered. This separation is achieved through the localized influence of each tetrahedron on 3D Gaussians, resulting in improved optimization. Using the cage-based deformation model, we introduce a compositional pipeline that decomposes an avatar into layers, such as garments, hands, or faces, improving the modeling of phenomena like garment sliding. These parts can be conditioned on different driving signals, such as keypoints for facial expressions or joint-angle vectors for garments and the body. Our experiments on two multi-view datasets with varied body shapes, clothes, and motions show higher-quality results. They surpass PSNR and SSIM metrics of other SOTA methods using the same data while offering greater flexibility and compactness.

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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. Instant Expressive Gaussian Head Avatars at Over 100 FPS

    cs.CV 2025-12 conditional novelty 7.0 of 10

    A single-photo avatar encoder with per-Gaussian feature-space deformation animates faces at 107 FPS with expression quality competitive with diffusion models.

  2. Pippo: High-Resolution Multi-View Humans from a Single Image

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A single-image multi-view diffusion transformer generates 1K-resolution turnaround views of humans, with attention biasing for many views and a new reprojection-error metric.

  3. DevilSight: Augmenting Monocular Human Avatar Reconstruction through a Virtual Perspective

    cs.CV 2025-08 reject novelty 5.0 of 10

    A monocular human avatar reconstruction method generates pseudo back-view videos with a fine-tuned diffusion model and uses them as extra training data for a 3D Gaussian avatar.

  4. Wavelet-GS: 3D Gaussian Splatting with Wavelet Decomposition

    cs.GR 2025-07 reject novelty 5.0 of 10

    Wavelet-GS splits a 3D point cloud into low- and high-frequency wavelet parts, trains each with its own strategy, plus a relight module, reporting gains over prior 3DGS variants on four datasets.

  5. SAT: Supervisor Regularization and Animation Augmentation for Two-process Monocular Texture 3D Human Reconstruction

    cs.CV 2025-08 conditional novelty 4.0 of 10

    A two-stage Gaussian-splatting framework with supervisor feature regularization and online animation augmentation improves monocular textured 3D human reconstruction on CustomHuman and THuman3.0.

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