SAGE: Sparse-View Human Compression via Source-Anchored Gaussian Reconstruction

Human multiview compression must preserve a person's appearance across camera queries while transmitting only a few captured views. A generative prior can synthesize missing viewpoints, but its inferred appearance and geometry may deviate from the captured subject. When decoded camera images and generated views jointly supervise a reconstruction, the more numerous generated views can dominate optimization and weaken captured detail. We propose SAGE, Source-Anchored Gaussian Encoding, a sparse-view human compression framework that codes selected images and geometric conditions, constructs a reusable Gaussian representation with a fixed prior, and renders later viewpoints from that representation. Its key mechanism is source anchoring: decoded captured views receive more frequent and stronger photometric supervision, while generated views extend coverage. A later reduction in generated-view weight further steers optimization toward observed appearance. On the evaluated DNA-Rendering captures, SAGE achieves the smallest average complete package and leads the compared methods on all four quality measures. On calibrated THuman2.0 scans, it improves foreground fidelity and perceptual quality over size-aligned MV-HEVC under a common Gaussian receiver. These results show how compact human-view transmission can exploit generated coverage while preserving the distinctive appearance evidence in coded observations across camera queries.