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We introduce AvatarBooth, a novel method for generating high-quality 3Davatars using text prompts or specific images. Unlike previous approaches thatcan only synthesize avatars based on simple text descriptions, our methodenables the creation of personalized avatars from casually captured face orbody images, while still supporting text-based model generation and editing.Our key contribution is the precise avatar generation control by using dualfine-tuned diffusion models separately for the human face and body. Thisenables us to capture intricate details of facial appearance, clothing, andaccessories, resulting in highly realistic avatar generations. Furthermore, weintroduce pose-consistent constraint to the optimization process to enhance themulti-view consistency of synthesized head images from the diffusion model andthus eliminate interference from uncontrolled human poses. In addition, wepresent a multi-resolution rendering strategy that facilitates coarse-to-finesupervision of 3D avatar generation, thereby enhancing the performance of theproposed system. The resulting avatar model can be further edited usingadditional text descriptions and driven by motion sequences. Experiments showthat AvatarBooth outperforms previous text-to-3D methods in terms of renderingand geometric quality from either text prompts or specific images. Please checkour project website at https://zeng-yifei.github.io/avatarbooth_page/.

AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation