3D Reconstruction and Generation Models

Leverage open-source libraries like Nerfstudio, Gaussian-Splatting, or Hugging Face Diffusers.

Setup#

pip install torch torchvision nerfstudio gaussian-splatting

# For inputs: diffusers transformers

Basic Image-to-3D Reconstruction#

Use models like Instant3D or Zero-1-to-3.

from diffusers import StableDiffusionPipeline
import torch

# Load a 3D-capable pipeline (e.g., adapted for depth)
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "A red sports car on a racetrack"
image = pipe(prompt).images[0]

# Post-process image to depth map (e.g., MiDaS)

# Then reconstruct 3D using feed-forward model

For advanced: Integrate Hunyuan-like models (if available on HF).

Feed-Forward World Generation#

# Pseudo-code for universal recon
from transformers import pipeline

generator = pipeline("text-to-3d", model="open-source-3d-model")

# Placeholder
outputs = generator(
    inputs={"text": "Urban park with benches", "image": image_path, "video": video_path},
    return_type="multi"

# point_cloud, depth_maps, splats
)

# Save outputs
point_cloud = outputs["point_cloud"]

# .ply format
splats = outputs["gaussian_splats"]

# For rendering

Gaussian Splatting Pipeline#

1. Input: Multi-view images.
2. Optimize: `python train.py -s data/inputs`.
3. Render: Novel views with real-time speeds.

Full Example: Generate asset from single image.

• Extract depth/normals.
• Fit Gaussians.
• Export PBR materials.