Feedforward Novel View Synthesis using Latent Representations for In-the-Wild Images

Recent feed-forward models have shown promise for novel view synthesis (NVS) from unconstrained photo collections. However, existing training setups offer limited coverage of real-world appearance variation, transient occlusion, or diverse source-view configurations, while differences in evaluation protocols complicate comparisons. We introduce the Wild-NVS dataset and benchmark, curated from internet photo collections, with a sampling strategy that constructs source–target view sets using bidirectional covisibility, joint target coverage, and viewpoint diversity. The benchmark provides scene-level splits and fixed evaluation protocols across multiple source-view counts. We also propose Wild-NVS, a feed-forward model that synthesizes target views from latent representations. It combines bottlenecked appearance conditioning with a learned transient prediction head that guides source-feature suppression before decoding. Experiments on Wild-NVS and PhotoTourism demonstrate state-of-the-art performance among deterministic feed-forward methods for in-the-wild NVS, with improved appearance modeling and reduced transient artifacts. Our models and dataset will be made public.