Sperid Labs open-sources Idol, a single-image-to-3D Gaussian splat model
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabSperid Labs released Idol, a model that converts one image of an object into a 3D Gaussian splat, generating up to 1,048,576 Gaussians including unseen sides. The repository states it removes backgrounds, frames the object, and writes a standard 3DGS .ply file. It reports 7.2 GB peak VRAM and sampling times under 2 seconds on consumer GPUs. A benchmark compares it against TripoSplat, TRELLIS, and LGM on PSNR, SSIM, LPIPS, IoU, DINOv2, and CLIP metrics, and reports per-GPU request times.
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Sperid Labs released Idol, a model that converts one image of an object into a 3D Gaussian splat, generating up to 1,048,576 Gaussians including unseen sides. The repository states it removes backgrounds, frames the object, and writes a standard 3DGS .ply file. It reports 7.2 GB peak VRAM and sampling times under 2 seconds on consumer GPUs. A benchmark compares it against TripoSplat, TRELLIS, and LGM on PSNR, SSIM, LPIPS, IoU, DINOv2, and CLIP metrics, and reports per-GPU request times.
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Key findings from the stored brief
- Generates up to 1,048,576 Gaussians from a single input image and exports a standard 3DGS .ply file.
- Reports 7.2 GB peak VRAM with about 3.7-4.4 GB of weights, using mostly FP16/bf16 precision.
- On its benchmark, Idol has the best SSIM, LPIPS, IoU, DINOv2, and CLIP scores among the compared models, but LGM has higher PSNR.
- Requires accepting gated terms for DINOv3 and FLUX.2-dev VAE on Hugging Face before first use.
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Sperid Labs released Idol under Apache-2.0: it turns one image of an object into up to about one million 3D Gaussians, including unseen sides, using 7.2 GB of VRAM and, it says, in under 2 seconds. Model posted Oct 28; launch post on X Oct 29 drew over 2,000 likes.
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- x post ↗ · 2,042 likes, 230 reposts, 81 replies, 66 quotes · observed 11/1/2026, 11:07:06 AM
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Why this may matter
An open-weight single-image-to-3D splat model that runs on consumer hardware lowers the barrier to 3D asset generation for researchers and developers. The reported benchmark and hardware timings give a concrete basis for comparison with other methods.
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Benchmark results are reported by the authors on their own setup; source claims not independently checked.
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