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• W4386076278 abstract "Recent years have witnessed the tremendous progress of 3D GANs for generating view-consistent radiance fields with photo-realism. Yet, high-quality generation of human radiance fields remains challenging, partially due to the limited human-related priors adopted in existing methods. We present HumanGen, a novel 3D human generation scheme with detailed geometry and 360° realistic free-view rendering. It explicitly marries the 3D human generation with various priors from the 2D generator and 3D reconstructor of humans through the design of “anchor image”. We introduce a hybrid feature representation using the anchor image to bridge the latent space of HumanGen with the existing 2D generator. We then adopt a pronged design to disentangle the generation of geometry and appearance. With the aid of the anchor image, we adapt a 3D reconstructor for fine-grained details synthesis and propose a two-stage blending scheme to boost appearance generation. Extensive experiments demonstrate our effectiveness for state-of-the-art 3D human generation regarding geometry details, texture quality, and free-view performance. Notably, HumanGen can also incorporate various off-the-shelf 2D latent editing methods, seamlessly lifting them into 3D." @default.
• W4386076278 created "2023-08-23" @default.
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• W4386076278 date "2023-06-01" @default.
• W4386076278 modified "2023-10-16" @default.
• W4386076278 title "HumanGen: Generating Human Radiance Fields with Explicit Priors" @default.
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• W4386076278 doi "https://doi.org/10.1109/cvpr52729.2023.01207" @default.
• W4386076278 hasPublicationYear "2023" @default.
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