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We present a new approach, termed GPS-Gaussian, for synthesizing novel viewsof a character in a real-time manner. The proposed method enables 2K-resolutionrendering under a sparse-view camera setting. Unlike the original GaussianSplatting or neural implicit rendering methods that necessitate per-subjectoptimizations, we introduce Gaussian parameter maps defined on the source viewsand regress directly Gaussian Splatting properties for instant novel viewsynthesis without any fine-tuning or optimization. To this end, we train ourGaussian parameter regression module on a large amount of human scan data,jointly with a depth estimation module to lift 2D parameter maps to 3D space.The proposed framework is fully differentiable and experiments on severaldatasets demonstrate that our method outperforms state-of-the-art methods whileachieving an exceeding rendering speed.

GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis