Optimizing Few-Shot Novel View Synthesis with Human-Perceptual Preferences.
Human Perceptual Preference Optimization is introduced, a framework that incorporates human perceptual guidance into model training and mitigates distortions by regularizing training dynamics with perceptual preference cues, thereby reducing the reliance on extensive supervised views.