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dreambooth3d dreambooth3d subject driven text to 3d generation amit raj srinivas kaza ben poole michael niemeyer nataniel ruiz ben mildenhall shiran zada kfir aberman michael rubinstein jonathan barron yuanzhen li varun jampani google personalized 3d models from just a few casual photos with text driven modifications paper summary video bibtex abstract we present dreambooth3d an approach to personalize text to 3d generative models from as few as 3 6 casually captured images of a subject our approach combines recent advances in personalizing text to image models dreambooth with text to 3d generation dreamfusion we find that naively combining these methods fails to yield satisfactory subject specific 3d assets due to personalized text to image models overfitting to the input viewpoints of the subject we overcome this through a 3 stage optimization strategy where we jointly leverage the 3d consistency of neural radiance fields together with the personalization capability of text to image models our method can produce high quality subject specific 3d assets with text driven modifications such as novel poses colors and attributes that are not seen in any of the input images of the subject approach our method takes as input 3 6 casual captures of a subject e g a specific dog and a text prompt and returns a volumetric model nerf respecting both the prompt and the subject we can use text prompts to synthesize the subjects under difference contexts in the stage 1 left we first partially train a dreambooth and use the resulting model to optimize the initial nerf in stage 2 middle we render multi view images along random viewpoints from the initial nerf and then translate them into pseudo multi view subject images using a fully trained dreambooth model in the final stage 3 right we further fine tune the partial dreambooth using multi view images and then use the resulting multi view dreambooth to optimize the final nerf 3d asset using the sds loss along with the multi view reconstruction loss subject guidance dreambooth3d can work on diverse subject types as illustrated by rare objects such as wodden owl here a photo of a candle photo of a wooden owl a photo of a sloth plushie photo of a wolf plushie material change our framework allows versatile 3d generation with simple modifications in the text prompts here we show some material editing results notice that the glass sculpture and the statue of the dog correspond to the given subject conditioning images a photo of a dog a dog made of glass a stone statue of a dog color change dreambooth 3d can introduce color changes in a consistent manner we use a prompt of the following format a backpack for these results where the caption of each image represents the conditioning images pink green grey pose variations we can even perform non rigid deformations such as pose changes relatively easily with simple modifications of text prompts here we show different dog subject results in various poses note that none of the exemplar images contain an image of a jumping or sleeping dog conditioning images photo of a dog sitting photo of a dog sleeping photo of a dog jumping cartoon to 3d dreambooth3d can even produce plausible 3d models from unrealistic cartoon images accessorization our model can add accessories directly onto the 3d subject or into the scene as in the rainbow carpet and green umbrella conditioning images photo of a dog sitting on a rainbow carpet sitting on a purple carpet wearing a green umbrella mesh extraction meshes can be extracted from the learnt volumetric representation using marching cube which can then be used as a traditional mesh asset in different applications and for 3d printing conditioning images stone statue of a dog 3d printed asset mesh summary results with geometry results for text based 3d asset generation conditioned on casual captures the assets below are generated using the text prompt a photograh of where is the subject repesented in the exemplar images the first column presents the exemplar images used to update our t2i model the second and third column shows two rendered views from the learnt volume the last three columns shows the depth normals and alpha maps for the second rendered view bibtex article raj2023dreambooth3d title dreambooth3d subject driven text to 3d generation author raj amit and kaza srinivas and poole ben and niemeyer michael and mildenhall ben and ruiz nataniel and zada shiran and aberman kfir and rubenstein michael and barron jonathan and li yuanzhen and jampani varun journal iccv year 2023