Guiding Image-to-3D Generation with Test-Time Partial Observations
Image-to-3D models can generate visually compelling 3D assets from a single RGB image, but their geometry is often only loosely constrained by the available observations, limiting their use in applications that require geometric fidelity. In many real-world settings, however, partial geometric observations of the object may be available at test time. We introduce a training-free framework for incorporating such evidence into pretrained image-to-3D generative models without retraining or finetuning. To do this, we guide generation using a ray-consistent observation likelihood defined over the m
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “Guiding Image-to-3D Generation with Test-Time Partial Observ” ≈ “Tongyi-MAI/Z-Image-Turbo””
- LinkedLinked via arxiv author · 85%Jerred Chen →
“Guiding Image-to-3D Generation with Test-Time Partial Observations”
- LinkedLinked via arxiv author · 85%Simon Weber →
“Guiding Image-to-3D Generation with Test-Time Partial Observations”
- LinkedLinked via arxiv author · 85%Ronald Clark →
“Guiding Image-to-3D Generation with Test-Time Partial Observations”