I am a research scientist at Adobe. Before that, I obtained my Ph.D. in Computer Science at the University of Rochester, under the supervision of Prof. Jiebo Luo. I am fortunate to have collaborated with Dr. Zhe Lin, Dr. Han Zhang, Dr. Ding Liu, Dr. Wenhan Luo, Dr. Lin Ma, and many excellent researchers during my internships.
My research interests include large generative models, diffusion models, personalized image
generation and editing, image shadow synthesis, object novel view synthesis, multi-modal visual synthesis,
and representation learning.
I am looking for a full-time research intern to join Adobe Photoshop Team this coming summer, aiming at top conference papers.
This internship will be focusing on multi-modal large language modeling (MLLM) for image editing/generation. Meanwhile, I am also looking for long-term university research collaborations,
where I can provide mentorship for research projects in the computer vision and natural language domains.
If you are interested, feel free to reach out with your resume.
Jun. 2022 -- I obtain my Ph.D from the University of Rochester. See my PhD thesis. Many thanks to my advisor Jiebo Luo, my mentors and collaborators, who have giving me this unforgettable experience.
I am recently focusing on large generative modeling and personalized content creation.
Representative works are highlighted. See full list in my Google Scholar.
we introduce SwapAnything, a novel framework that can swap any objects in an image with personalized concepts given by the reference, while keeping the context unchanged.
Our work achieves advanced image composition with a decent identity preservation, automatic object viewpoint/pose adjustment, color and lighting harmonization, and shadow synthesis. All these effects are achieved in a single framework!
We introduce Relightful Harmonization, a lighting-aware diffusion model designed to seamlessly harmonize sophisticated lighting effect for the foreground portrait using any background image.
We propose InstantBooth, a novel approach built upon pre-trained text-to-image models that enables fast personalized text-to-image generation without test-time finetuning.
My PhD thesis summarizes my main research works on Guided Visual Content Creation during my PhD program. Part I introduces guidance-driven visually pleasing data synthesis. Part II presents guidance-driven synthesis for downstream visual recognition tasks.
We are among the few pioneering works on unsupervised real-world low-light image enhancement. Specifically, we tackle the problem of enhancing
real-world low-light images with significant noise in an unsupervised fashion. To this end, we explicitly
decouple this task into two sub-problems: illumination enhancement
and noise suppression.
We adopt a temporal variational auto-encoder (T-VAE) model for bone osteolysis prediction
on computed tomography (CT) images of murine breast cancer bone metastases.
We introduce an important but still unexplored research task Image Sentiment Transfer and propose an effective
and flexible framework that performs image sentiment transfer at both the image level and the object level.
We tackle a challenging exemplar-guided image synthesis task, where the exemplar providing the style guidance is an arbitrary scene
image which is semantically different from the given pixel-wise label map.
We aim at transforming an image with a fine-grained
category to synthesize new images that preserve the identity of the input image, which can thereby benefit the subsequent fine-grained image recognition and few-shot learning tasks.
We frame the caricature generation task as a weakly
paired image-to-image translation task, and propose CariGAN model to generate high-fidelity caricature images from human faces with proper exaggerations.
We propose a foreground-aware image inpainting system that explicitly disentangles structure inference and content completion. Our model first learns to predict
the foreground contour, and then inpaints the missing region using the predicted contour as guidance.
We propose a two-stage GAN model to generate vivid yet content-preserving time-lapse videos from only a single starting frame. To this end, we desentangle the task into content generation and motion enhancement.
We proposes an unsupervised feature learning model, named the Stacked Convolutional Denoising Auto-Encoders,
that can map an image to hierarchical representations without
any label information.
We propose a group regularization method, Structured Decorrelation Constraint (SDC), that regularizes the activations of the hidden layers in groups to achieve better generalization.
Work Experience
Research Scientist (Full-Time) at Adobe (2022 - now)
Location: San Jose, CA.
Project: Image synthesis and editing.
Research Intern at Google Research (2021)
Location: Mountain View, CA.
Project: Example guided image inpainting.
Research Intern at Google Cloud (2020)
Location: Sunnyvale, CA.
Mentors: Mingyang Ling, Han Zhang, Zizhao Zhang.
Project: Augmenting data with generative models for downstream recognition tasks.
