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arXiv · 2511.08203

Twist and Compute: The Cost of Pose in 3D Generative Diffusion

Kyle Fogarty·Jack Foster·Boqiao Zhang·Jing Yang·Cengiz Öztireli
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Abstract

Despite their impressive results, large-scale image-to-3D generative models remain opaque in their inductive biases. We identify a significant limitation in image-conditioned 3D generative models: a strong canonical view bias. Through controlled experiments using simple 2D rotations, we show that the state-of-the-art Hunyuan3D 2.0 model can struggle to generalize across viewpoints, with performance degrading under rotated inputs. We show that this failure can be mitigated by a lightweight CNN that detects and corrects input orientation, restoring model performance without modifying the generative backbone. Our findings raise an important open question: Is scale enough, or should we pursue modular, symmetry-aware designs?

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Kyle Fogarty, Jack Foster, Boqiao Zhang, Jing Yang, Cengiz Öztireli. 2025-11-11. Twist and Compute: The Cost of Pose in 3D Generative Diffusion. https://arxiv.org/abs/2511.08203

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