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Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video

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arxiv 2409.08189 v1 pith:RHMNM2KN submitted 2024-09-12 cs.CV cs.GR

Boxiang Rong , Artur Grigorev , Wenbo Wang , Michael J. Black , Bernhard Thomaszewski , Christina Tsalicoglou , Otmar Hilliges This is my paper
classification cs.CVcs.GR
keywords garmentsgaussiangarmentmulti-viewfine-tunedreconstructingsimulation-readyvideos
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We introduce Gaussian Garments, a novel approach for reconstructing realistic simulation-ready garment assets from multi-view videos. Our method represents garments with a combination of a 3D mesh and a Gaussian texture that encodes both the color and high-frequency surface details. This representation enables accurate registration of garment geometries to multi-view videos and helps disentangle albedo textures from lighting effects. Furthermore, we demonstrate how a pre-trained graph neural network (GNN) can be fine-tuned to replicate the real behavior of each garment. The reconstructed Gaussian Garments can be automatically combined into multi-garment outfits and animated with the fine-tuned GNN.

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Cited by 4 Pith papers

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

  1. InverseDraping: Recovering Sewing Patterns from 3D Garment Surfaces via BoxMesh Bridging

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    A two-stage autoregressive framework centered on BoxMesh recovers parametric sewing patterns from 3D garment surfaces, claiming state-of-the-art results on benchmarks and generalization to real scans and single-view images.

  2. ReWeaver: Towards Simulation-Ready and Topology-Accurate Garment Reconstruction

    cs.CV 2026-01 unverdicted novelty 7.0 of 10

    ReWeaver reconstructs topology-accurate 3D garments and sewing patterns from sparse multi-view images by predicting seams and panels in 2D UV and 3D space using a new 100k-sample synthetic dataset.

  3. SAFT: Shape and Appearance of Fabrics from Template via Differentiable Physical Simulations from Monocular Video

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A physics-based shape-from-template method with two regularization terms reduces cloth reconstruction error by about 2.6x versus prior work and enables SVBRDF and lighting recovery from monocular video.

  4. Fashion-3DLR: A Controllable 3D Garment Generation Using Pairwise Fashion Elements for Intelligent Design

    cs.CV 2026-07 reject novelty 5.0 of 10

    A 3D garment generation framework that fuses sketch and texture conditions via a diffusion transformer, outputting simulation-capable 3D Gaussians and meshes.

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