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              <p>Realistic 3D animal generation from text prompts is a significant yet challenging task. Traditional approaches, which use score distillation sampling to optimize 3D formats like meshes or neural fields, often suffer from a lack of detail and designed for fixed shape. To address both limitations, in this work, we introduce DreamPet, a novel framework that explores a retrieval-augmented approach tailored for score distillation and efficiently produces high-quality 3D animal models featuring fine-grained geometry and lifelike textures. Our key insight is that both expressiveness of 2D diffusion models and geometric consistency of 3D animal assets can be fully leveraged by employing the semantically relevant assets directly within the optimization process. Specifically, our method features 1) a Shape-Aware SDS for optimizing appearance and geometry to ensure structural consistency per category, and 2) a Category aware refinement module that addresses the over-saturation issue and further eliminates floating artefacts based on the animal category to produce realistic textures. Extensive experiments demonstrate competitive quality of our method, rendering 3D animals under diverse scenarios.</p>
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