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We present DreamAvatar, a text-and-shape guided framework for generatinghigh-quality 3D human avatars with controllable poses. While encouragingresults have been reported by recent methods on text-guided 3D common objectgeneration, generating high-quality human avatars remains an open challenge dueto the complexity of the human body's shape, pose, and appearance. We proposeDreamAvatar to tackle this challenge, which utilizes a trainable NeRF forpredicting density and color for 3D points and pretrained text-to-imagediffusion models for providing 2D self-supervision. Specifically, we leveragethe SMPL model to provide shape and pose guidance for the generation. Weintroduce a dual-observation-space design that involves the joint optimizationof a canonical space and a posed space that are related by a learnabledeformation field. This facilitates the generation of more complete texturesand geometry faithful to the target pose. We also jointly optimize the lossescomputed from the full body and from the zoomed-in 3D head to alleviate thecommon multi-face ''Janus'' problem and improve facial details in the generatedavatars. Extensive evaluations demonstrate that DreamAvatar significantlyoutperforms existing methods, establishing a new state-of-the-art fortext-and-shape guided 3D human avatar generation.

DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models