Improve multi-view consistency and geometric accuracy

Improve view consistency and geometric accuracy in multi-view image generation, where these objectives remain unresolved challenges across existing approaches.

Background

The paper surveys multi-view image-generation methods, including approaches that use correspondence-aware attention, factor-graph decomposition, and geometric relationships across views. Despite these advances, existing systems can still produce duplicated objects, repetitive textures, and other inconsistencies between views.

The unresolved problem concerns simultaneously achieving reliable cross-view consistency and accurate geometric correspondence in generated multi-view images. StreetDiff is proposed as a response to this challenge in complex urban street scenes, but the quoted passage identifies the broader research problem rather than a problem fully resolved by the paper.

References

These approaches provide valuable insights into multi-view diffusion models, yet improving view consistency and geometric accuracy remains an open challenge.

— StreetDiff: Multi-view Street Scenes Generation via Cross-view Consistent Multi-view Stable Diffusion with Structure Prompts  (2609.09890 - Zhang et al., 9 Sep 2026) in Section 2, Related Work, subsection “Multi-view Generation”

Better anchors and geometric-uncertainty modeling remain open directions.

— SymRegFlow: Symmetry-Regularized Flow Matching for Video World Models  (2610.02726 - Ye et al., 2 Oct 2026) in Conclusion and Discussion, Limitations

However, how to avoid self-occlusion that fundamentally distorts the perceived 3D shape semantics still remains an open problem.

— Revisiting Triplet Contrastive Learning for 3D Shape Understanding  (2610.19519 - Zhu et al., 14 Oct 2026) in Section 1, Introduction