The paper proves that locally orthogonal directions in generative models guarantee latent factor identifiability without needing statistical independence or causal assumptions. Experiments with orthogonality-regularized normalizing flows confirm reliable recovery of true latent factors, challenging prior claims about unsupervised disentanglement impossibility.