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On the recordRead 7 Oct 202625 facts
Wonder3D
On the record
- Contact and collaboration
- The project invites collaboration and lists [email protected] for contact.github.com · read 7 Oct 2026
- Generation
- It generates consistent multi-view normal maps and corresponding color images using cross-domain diffusion.github.com · read 7 Oct 2026
- Generation method
- It generates consistent multi-view normal maps and corresponding color images with a cross-domain diffusion model, then uses normal fusion for reconstruction.github.com · read 7 Oct 2026
- Hardware
- The example pipeline moves to CUDA when available, and the repository provides a one-GPU inference configuration.github.com · read 7 Oct 2026
- Input guidance
- The README says front-facing images with fewer occlusions generally produce better reconstructions.github.com · read 7 Oct 2026
- Input preparation
- The README suggests using Clipdrop or rembg to segment the foreground object.github.com · read 7 Oct 2026
- Integrations
- The documented workflow uses Hugging Face Diffusers and checkpoints, and describes using SAM and Clipdrop or rembg for foreground masks.github.com · read 7 Oct 2026
- Intended uses
- The project page shows examples involving Houdini, 3D asset creation, 3D printing, 2D designs, and animal images.xxlong.site · read 7 Oct 2026
- License
- The repository states that Wonder3D is under the MIT license and free to use with acknowledgement.github.com · read 7 Oct 2026
- Local demos
- The repository provides local Gradio demos for generating normals and colors and for mesh reconstruction.github.com · read 7 Oct 2026
- Mesh extraction
- It provides Instant-NSR and NeuS-based mesh extraction workflows.github.com · read 7 Oct 2026
- Model dependencies
- The inference instructions call for downloaded checkpoints and a Segment Anything Model checkpoint.github.com · read 7 Oct 2026
- Multi-view attention
- The method uses multi-view cross-domain attention to exchange information across views and image modalities.xxlong.site · read 7 Oct 2026
- Notable limits
- The README says the implementation supports six views and 256×256 generation, and resizes input images to that resolution.github.com · read 7 Oct 2026
- Output limits
- The current implementation supports six views and resizes input images to 256×256 for generation.github.com · read 7 Oct 2026
- Processing time
- The project says reconstruction takes about 2–3 minutes.xxlong.site · read 7 Oct 2026
- Purpose
- Wonder3D reconstructs textured 3D meshes from a single-view image.github.com · read 7 Oct 2026
- Reconstruction caveat
- The README says occluded inputs can produce worse reconstructions because six views may not cover the complete object.github.com · read 7 Oct 2026
