WildSplat: Feedforward Gaussian Splatting from Unposed In-the-Wild Images

While feedforward 3D reconstruction excels at efficient novelview synthesis, it typically falters when faced with scenes under varyingillumination. To this end, we introduce WildSplat, the first feedforward3D Gaussian Splatting framework capable of appearance-conditionednovel-view synthesis for unposed in-the-wild images. To handle inconsis-tent photometric conditions, we propose a dual-branch architecture thatexplicitly decouples geometry from appearance. The geometry branchextracts an appearance-invariant 3D structure and jointly predicts cam-era poses. To govern the rendering appearance, the appearance branchinjects target appearance cues into the content features via a globallypre-modulated cross-attention mechanism. To further prevent featureentanglement, we introduce a joint multi-reference training strategy thatstabilizes the training process. Extensive experiments show that Wild-Splat surpasses existing optimization-based and feedforward methods,achieving state-of-the-art performance in in-the-wild novel view synthesisand appearance editing from sparse inputs in a single forward pass.