Morphable Diffusion: 3D-Consistent Diffusion for Single-image Avatar Creation
CoRR(2024)
摘要
Recent advances in generative diffusion models have enabled the previously
unfeasible capability of generating 3D assets from a single input image or a
text prompt. In this work, we aim to enhance the quality and functionality of
these models for the task of creating controllable, photorealistic human
avatars. We achieve this by integrating a 3D morphable model into the
state-of-the-art multiview-consistent diffusion approach. We demonstrate that
accurate conditioning of a generative pipeline on the articulated 3D model
enhances the baseline model performance on the task of novel view synthesis
from a single image. More importantly, this integration facilitates a seamless
and accurate incorporation of facial expression and body pose control into the
generation process. To the best of our knowledge, our proposed framework is the
first diffusion model to enable the creation of fully 3D-consistent,
animatable, and photorealistic human avatars from a single image of an unseen
subject; extensive quantitative and qualitative evaluations demonstrate the
advantages of our approach over existing state-of-the-art avatar creation
models on both novel view and novel expression synthesis tasks.
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