Pixal3D — image-to-3D with PBR materials (full precision)

High-fidelity image-to-mesh with material baked in. ≈85–120 s per asset on an RTX 5090, returning ≈15–17 MB .glb files that already carry shading.

What this repo is: the Pixal3D pipeline at full precision — the three 1.3B transformers as ≈5.5 GB bf16 safetensors under ckpts/. Weights only, not a retrain. A GGUF build of the same weights (≈2.7 GB per transformer) lives in ChrisColeTech/Pixal3D-GGUF.

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Samples

Single image in, textured mesh out. Source images were rendered with Z-Image Turbo; the meshes are Blender Workbench renders of the raw .glb — no cleanup, no retopology, no separate paint stage.

Note on these samples: they were rendered from the GGUF build of these weights, not from the full-precision files in this repo. Timings and mesh sizes come from that run. The architecture and settings are identical; expect the full-precision path to differ only marginally, but the numbers below are not measured on it.

Turntable

Four views, 90° apart.

The chest display and dial, the ear knobs and the boot flare all survive, and the surface reads as brushed metal — material is produced with the geometry rather than in a later pass.

Recommended settings

Supported modes: img2mesh, txt2mesh, and the _textured variants

Notes and gotchas

• ⚠ Meshes export rotated 180° relative to some other generators. Pixal3D puts the subject's front along the opposite axis from TripoSG/TRELLIS.2, so a fixed camera that frames those correctly will render Pixal3D output from behind. If you batch-render thumbnails, orbit the camera 180° (or check one contact sheet per model before trusting a shared preset).
• Material comes for free — no separate paint stage needed, which is the main reason to choose this over a geometry-only model.
• Loads in a low-VRAM mode by default, reporting ≈0 GiB resident after load and streaming weights in as it runs. Generation still wants the card largely to itself; sharing a GPU with another model-serving process produces failures that look like crashes rather than clean OOM errors.
• Sparse-conv backend — logs [SPARSE] Conv backend: flex_gemm; Attention backend: flash_attn on load and pulls a valeoai/NAF estimator from torch.hub on first run, so a fresh machine needs network access.

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Files

For roughly half the download and resident footprint, use the GGUF build: ChrisColeTech/Pixal3D-GGUF.

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Provenance

• Upstream base model: Pixal3D (TencentARC)
• This build: redistributed as-is at full precision. Weights are not retrained here.
• License: MIT, per upstream.