Intervenable Particle Representations for Conditional Image Reconstruction

We introduce Particle-U-Net (PUN), a generalization of neural particle automata (NPA). PUN carries particle self-organization from pixel space into the spatial latent space of a frozen non-compressing autoencoder. It injects target-derived features into the particle update via feature-wise linear modulation and reads the resulting particle render into an image with a residual U-Net. NPA learns one rule per target and does not reach high fidelity on natural images. PUN removes both limitations: it handles natural images, generalizes to unseen faces without per-image optimization, and recovers image fidelity that pixel-space fitting does not reach. Beyond reconstruction, we study the explicit particle representation through recovery, coordinate interventions, and component comparisons. Position updates improve recovery from localized damage. Localization after imposed coordinate transformations depends on the spatial FiLM condition during subsequent updates. We also apply directional CLIP guidance to the pixel-domain particle update rule, asking whether the learned dynamics can be steered toward attribute-level edits.