Google uses Veo generative AI to create shoppable 3D product visualizations from 2D product images for Google Shopping
Online shopping cannot replicate the tactile, hands-on experience of physical stores, and creating high-quality 3D product visualization tools at scale has been costly and time-consuming for businesses.
The first-generation NeRF approach suffered from noisy camera pose signals and struggled with thin-structured products like sandals and heels; both early approaches required estimating precise camera poses from sparse images.
The Veo-based third-generation approach generates interactive 3D views from as few as three product images, generalizes across furniture, apparel, and electronics, and is already deployed on Google Shopping without requiring precise camera pose estimation.
Frequently asked questions
What did this team achieve with this AI workflow?
The Veo-based third-generation approach generates interactive 3D views from as few as three product images, generalizes across furniture, apparel, and electronics, and is already deployed on Google Shopping without re…
What tools did this team use?
Veo, Neural Radiance Fields (NeRF), view-conditioned diffusion model.
What results were reported?
Minimum product images required: three; Training dataset size: millions of high quality, 3D synthetic assets; Hallucination reduction: reduce hallucinations (source-reported, not independently verified).
What failed first in this deployment?
The first-generation NeRF approach suffered from noisy camera pose signals and struggled with thin-structured products like sandals and heels; both early approaches required estimating precise camera poses from sparse…
How is this ecommerce ops AI workflow structured?
Product images submitted → Veo generates 360° spin → Novel views with lighting capture → Interactive 3D views published.