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2026-02-18 11:34:42
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HumanNorm Human N o r m : Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation Xin Huang *,†,1 Ruizhi Shao *,2 Qi Zhang 1 Hongwen Zhang 2 Ying Feng 1 Yebin Liu 2 Qing Wang 1 1 Northwestern Polytechnical University 2 Tsinghua University * Equal Contribution, † Work done during an internship at Tsinghua University CVPR 2024 Given a prompt, our method is capable of generating a high-quality and realistic 3D human... [Paper] [Gallery] [Code] Methodology Our method is designed for high-quality and realistic 3D human generation from given prompts. The whole framework consists of geometry and texture generation. We first propose the normal-adapted and depth-adapted diffusion model for the geometry generation. These two models can guide the rendered normal and depth maps to approach the learned distribution of high-fidelity normal and depth maps through the SDS loss, thereby achieving high-quality geometr...
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