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Official Repository of [CVPR'24 Highlight Diffportrait3D: Controllable Diffusion for Zero-Shot Portrait View Synthesis]

Home Page: https://freedomgu.github.io/DiffPortrait3D/

License: MIT License

Python 99.91% Shell 0.09%
3d-aware-image-synthesis diffusion-models generative-ai novel-view-synthesis

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diffportrait3d's Issues

Random indentities used for Icam? One or many?

Hi,

Thank you for releasing the code.

I am confused by the setting of Icam. The paper said

Herein, appearance leakage from the view condition image will be reflected on the novel view synthesis during inference. This artifact is more pronounced when Iref and Icam exhibit distinct appearance features.

and

"Instead, we utilize a portrait image from a distinct random identity as the view condition, and generate novel-view images that mirror the head pose as in the condition portrait Icam."

Do you control the identity of Icam? Or simply generate Icam with random identities in training?

Thanks.

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