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Novel View Synthesis (NVS), which tries to produce a realistic image at thetarget view given source view images and their corresponding poses, is afundamental problem in 3D Vision. As this task is heavily under-constrained,some recent work, like Zero123, tries to solve this problem with generativemodeling, specifically using pre-trained diffusion models. Although thisstrategy generalizes well to new scenes, compared to neural radiancefield-based methods, it offers low levels of flexibility. For example, it canonly accept a single-view image as input, despite realistic applications oftenoffering multiple input images. This is because the source-view images andcorresponding poses are processed separately and injected into the model atdifferent stages. Thus it is not trivial to generalize the model intomulti-view source images, once they are available. To solve this issue, we tryto process each pose image pair separately and then fuse them as a unifiedvisual representation which will be injected into the model to guide imagesynthesis at the target-views. However, inconsistency and computation costsincrease as the number of input source-view images increases. To solve theseissues, the Multi-view Cross Former module is proposed which mapsvariable-length input data to fix-size output data. A two-stage trainingstrategy is introduced to further improve the efficiency during training time.Qualitative and quantitative evaluation over multiple datasets demonstrates theeffectiveness of the proposed method against previous approaches. The code willbe released according to the acceptance.

VaLID: Variable-Length Input Diffusion for Novel View Synthesis