Explain the source of explicit-conditioning benefits for visual memory

Determine whether the stronger visual-memory performance of 4D foundation models that explicitly project input observations onto target views before inpainting is caused by explicit memory conditioning simplifying invisible-segment generation, given that these models are predominantly trained on videos where target objects remain continuously visible.

Background

PersistBench evaluates object permanence, motion continuity, and appearance preservation for 4D foundation models when target objects leave the input field of view. The results report that models using explicit geometric or projected-view conditioning, including GEN3C, TrajectoryCrafter, and NeoVerse, generally perform better on invisible segments than models without such conditioning.

The paper attributes this performance difference to a conjectured interaction between training-data distribution and architecture: most current models are trained on videos in which target objects remain visible, whereas explicit conditioning may provide direct guidance for reconstructing unseen content. The causal explanation remains conjectural rather than established, making it an unresolved question for future model analysis and training studies.

References

We conjecture this is because most 4D models are predominantly trained on videos where target objects remain continuously visible.

— Can 4D Foundation Models Remember?  (2609.20819 - He et al., 17 Sep 2026) in Section 5.1, subsection “Key Findings,” paragraph “Explicit conditioning helps visual memory”

Architectures that condition on explicit geometric structure partially compensate for this by encoding object persistence directly into the input, but it remains unclear whether such architectural advantages would persist under a memory-aware training paradigm.

— Can 4D Foundation Models Remember?  (2609.20819 - He et al., 17 Sep 2026) in Section 6, Conclusion