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Large-scale pre-trained vision-language models allow for the zero-shottext-based generation of 3D avatars. The previous state-of-the-art methodutilized CLIP to supervise neural implicit models that reconstructed a humanbody mesh. However, this approach has two limitations. Firstly, the lack ofavatar-specific models can cause facial distortion and unrealistic clothing inthe generated avatars. Secondly, CLIP only provides optimization direction forthe overall appearance, resulting in less impressive results. To address theselimitations, we propose AvatarFusion, the first framework to use a latentdiffusion model to provide pixel-level guidance for generating human-realisticavatars while simultaneously segmenting clothing from the avatar's body.AvatarFusion includes the first clothing-decoupled neural implicit avatar modelthat employs a novel Dual Volume Rendering strategy to render the decoupledskin and clothing sub-models in one space. We also introduce a noveloptimization method, called Pixel-Semantics Difference-Sampling (PS-DS), whichsemantically separates the generation of body and clothes, and generates avariety of clothing styles. Moreover, we establish the first benchmark forzero-shot text-to-avatar generation. Our experimental results demonstrate thatour framework outperforms previous approaches, with significant improvementsobserved in all metrics. Additionally, since our model is clothing-decoupled,we can exchange the clothes of avatars. Code are available on our project pagehttps://hansenhuang0823.github.io/AvatarFusion.

AvatarFusion: Zero-shot Generation of Clothing-Decoupled 3D Avatars Using 2D Diffusion