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TELA: Text to Layer-wise 3D Clothed Human Generation

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arxiv 2404.16748 v1 pith:G7KM7R22 submitted 2024-04-25 cs.CV

classification cs.CV
keywords humangenerationclothedbodyclothinglayer-wisemodelachieves
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper addresses the task of 3D clothed human generation from textural descriptions. Previous works usually encode the human body and clothes as a holistic model and generate the whole model in a single-stage optimization, which makes them struggle for clothing editing and meanwhile lose fine-grained control over the whole generation process. To solve this, we propose a layer-wise clothed human representation combined with a progressive optimization strategy, which produces clothing-disentangled 3D human models while providing control capacity for the generation process. The basic idea is progressively generating a minimal-clothed human body and layer-wise clothes. During clothing generation, a novel stratified compositional rendering method is proposed to fuse multi-layer human models, and a new loss function is utilized to help decouple the clothing model from the human body. The proposed method achieves high-quality disentanglement, which thereby provides an effective way for 3D garment generation. Extensive experiments demonstrate that our approach achieves state-of-the-art 3D clothed human generation while also supporting cloth editing applications such as virtual try-on. Project page: http://jtdong.com/tela_layer/

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Cited by 3 Pith papers

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

  1. Disentangled Clothed Avatar Generation with Layered Representation

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A feed-forward diffusion model generates fully disentangled clothed avatars by representing body, hair, and clothing in separate layers of a Gaussian-based UV feature plane.

  2. PhyCAGE: Physically Plausible Compositional 3D Asset Generation from a Single Image

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A single-image pipeline that generates physically plausible compositional 3D Gaussian Splatting assets by using a physics simulator as a gradient-driven optimizer.

  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.

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