AI RESEARCH
Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction
arXiv CS.CV
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ArXi:2605.31595v1 Announce Type: new Dynamic scene reconstruction from monocular video remains a fundamental challenge in computer vision. Existing feed-forward methods predict 3D Gaussians pixel-wise for each frame, suffering from duplicated Gaussians and view-dependent biases that hinder effective learning of scene motion. We present C4G, a feed-forward 4D reconstruction framework built upon a compact set of timestamp-conditioned learnable Gaussian query tokens.