DiffGI: Differentiable Geometry Images for High-Fidelity Thin-Shell 3D Generation

Existing 3D generative models predominantly rely on implicit volumetric representations, which inherently enforce watertight topology and struggle to faithfully represent thin-shell and non-manifold geometries such as garments. While geometry image-based approaches o(cid:27)er a surface-centric alternative, existing methods typically rely on discrete binary occupancy maps whose resolution-dependent boundary encoding causes staircase artifacts and information loss upon downsampling, while surface reconstruction remains a non-di(cid:27)erentiable postprocessing step disconnected from the learning pipeline. 1 To address this, we propose Di(cid:27)erentiable Geometry Image (Di(cid:27)GI) , an end-to-end 3D-to-2D mapping framework that seamlessly integrates surface representation and geometric optimization. Di(cid:27)GI replaces conventional binary maps with a continuous 2D Truncated Signed Distance Function (TSDF), which encodes boundary position at subpixel precision within a (cid:28)xed grid resolution, e(cid:27)ectively eliminating resolutiondependent staircase artifacts even under aggressive downsampling. Building on this continuous (cid:28)eld, we introduce a di(cid:27)erentiable Marching Squares algorithm based on analytical linear interpolation, allowing gradients from 3D surface losses to propagate seamlessly back to the 2D latent space. Leveraging this di(cid:27)erentiable pipeline, we train a Di(cid:27)GI-VAE augmented with a geometry-aware normal rendering loss to compress complex 3D surfaces into an ultra-compact 32 × 32 latent space. Finally, we instantiate a transformer-based latent di(cid:27)usion model on top of this space for conditional 3D generation, showing that the proposed representation readily supports e(cid:30)cient generative modeling. Extensive experiments on garment and object datasets demonstrate that our method achieves superior reconstruction (cid:28)delity and boundary precision compared to prior geometry-image and voxel-based approaches, while requiring signi(cid:28)cantly fewer computational resources. • •