aigc3d/LHM

Health v2 · maintenance only

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Adoption not part of the score

2664 stars · 213 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

LHM is a PyTorch-based large reconstruction model that reconstructs high-fidelity animatable 3D human avatars from a single image in seconds, representing them as 3D Gaussian splatting via a multimodal transformer. It includes pretrained models, a ComfyUI integration, and memory-saving variants that run on consumer GPUs.

Use cases

• reconstruct an animatable 3D human avatar from a single photo
• generate rigged 3D digital humans for animation in seconds
• create 3D Gaussian splatting avatars without post-processing
• animate a reconstructed human with extracted motion sequences
• run 3D human reconstruction on consumer GPUs with limited memory
• integrate human reconstruction into ComfyUI workflows

When to choose

• you need fast feed-forward animatable human reconstruction from one image
• you want 3D Gaussian splatting avatars without optimization-based refinement
• you have a consumer GPU and need memory-efficient inference
• you want a ComfyUI node for human reconstruction and animation

When to avoid

• you need multi-view or video-based high-fidelity capture pipelines
• you require full rigging and editing in DCC tools like Blender or Maya
• you have no GPU available
• you need general object reconstruction rather than humans

Facets

library · maturity active

machine-learning deep-learning image-processing graphics simulation computer-vision graphics artificial-intelligence deep-learning python windows 3d-human-reconstruction gaussian-splatting animatable-avatars single-image-to-3d digital-human comfyui pytorch iccv2025 gpu linux docker

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markdown · JSON · MCP: product_card(name="aigc3d/LHM")