Zongze Wu

I'm a research scientist/engineer at Adobe Research in San Francisco.

At Adobe, I work with FireFly team for Structure Reference.

I got my PhD degree at Hebrew University of Jerusalem in 2022, under supervision of Prof. Dani Lischinski and Eli Shechtman from Adobe Research. I got my bachelor degree at Tongji University in 2016.

Email / CV / Scholar / Twitter

Research

My main interests are in generative modelings (GenAI), including diffusion model, GAN, and autoregressive model. I work on multi-modality generation/editing tasks, including image, video and text.

	Turboedit: Instant text-based image editing. Zongze Wu, Nicholas Kolkin, Jonathan Brandt, Richard Zhang, Eli Shechtman ECCV 2024, project page / arXiv / Video Users can upload an image, and edit the image with natural language. Each edit only takes half a second.
	Lazy diffusion transformer for interactive image editing. Yotam Nitzan, Zongze Wu, Richard Zhang, Eli Shechtman, Daniel Cohen-Or, Taesung Park, Michaël Gharbi ECCV 2024 project page / arXiv Instead of generating the entire image, we only generate the mask region to facilitate fast inpaint task.
	Third time’s the charm? image and video editing with stylegan3. Yuval Alaluf, Or Patashnik, Zongze Wu, Asif Zamir, Eli Shechtman, Dani Lischinski, Daniel Cohen-Or, AIM ECCVW 2022, project page / arXiv We show StyleGAN3 can be trained with unaligned image, and its w/w+ spaces are entangled than StyleGAN2.
	Stylealign: Analysis and applications of aligned stylegan models. Zongze Wu, Yotam Nitzan, Eli Shechtman, Dani Lischinski ICLR 2022 (Oral Presentation) project page / arXiv The child model's latent spaces are semantically aligned with its parent's, inheriting incredibly rich semantics.
	Styleclip: Text-driven manipulation of stylegan imagery. Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, Dani Lischinski ICCV 2021 (Oral Presentation) project page / arXiv / ICCV Video / Demo Video Text-based image editing through mapping CLIP space to StyleGAN latent space.
	Stylespace analysis: Disentangled controls for stylegan image generation. Zongze Wu, Dani Lischinski, Eli Shechtman CVPR 2021* (Oral Presentation) project page / arXiv / Video The space of channel-wise style parameters is significantly more disentangled than the other intermediate latent spaces in StyleGAN.
	Fine-grained foreground retrieval via teacher-student learning. Zongze Wu, Dani Lischinski, Eli Shechtman WACV 2021* arXiv / Video Retrieve foreground images that are semantically compatible with the background.

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