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Recent months have witnessed rapid progress in 3D generation based ondiffusion models. Most advances require fine-tuning existing 2D StableDiffsuions into multi-view settings or tedious distilling operations and hencefall short of 3D human generation due to the lack of diverse 3D human datasets.We present an alternative scheme named MVHuman to generate human radiancefields from text guidance, with consistent multi-view images directly sampledfrom pre-trained Stable Diffsuions without any fine-tuning or distilling. Ourcore is a multi-view sampling strategy to tailor the denoising processes of thepre-trained network for generating consistent multi-view images. It encompassesview-consistent conditioning, replacing the original noises with``consistency-guided noises'', optimizing latent codes, as well as utilizingcross-view attention layers. With the multi-view images through the samplingprocess, we adopt geometry refinement and 3D radiance field generation followedby a subsequent neural blending scheme for free-view rendering. Extensiveexperiments demonstrate the efficacy of our method, as well as its superiorityto state-of-the-art 3D human generation methods.

MVHuman: Tailoring 2D Diffusion with Multi-view Sampling For Realistic 3D Human Generation