MagicGarment: Benchmarking Single-Image Standalone 3D Garment Reconstruction
We introduce MagicGarment, a benchmark for single-image standalone 3D garment reconstruction with 1,518 professionally constructed assets across 17 composite garment types, aligned multimodal annotations, and a unified evaluation protocol.
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Key points
- Single-image 3D reconstruction has advanced rapidly, yet standalone garments remain challenging due to thin-shell geometry, open boundaries, and layered structures.
- Existing datasets and evaluation protocols provide limited support for systematic evaluation in this setting.
- We further establish a garment-aware reference baseline by adapting a pretrained image-to-3D backbone at two levels: Grounded Semantic Refinement (GSR) provides garment-focused visual conditioning, while Flow-Time-Route LoRA (FTR-LoRA) adapts the frozen 3D generative backbone using flow-timestep, token-routing, and expert-load information.
- Experiments demonstrate MagicGarment's benchmark utility and the effectiveness of garment-aware adaptation.
Sources (1)
- [1]MagicGarment: Benchmarking Single-Image Standalone 3D Garment ReconstructionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 05:45 AM
We introduce MagicGarment, a benchmark for single-image standalone 3D garment reconstruction with 1,518 professionally constructed assets across 17 composite garment types, aligned multimodal annotations, and a unified evaluation protocol.
Single-image 3D reconstruction has advanced rapidly, yet standalone garments remain challenging due to thin-shell geometry, open boundaries, and layered structures.
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