Publications (10)
IntroStyle: Training-Free Introspective Style Attribution using Diffusion Features
Anand Kumar, Jiteng Mu, Nuno Vasconcelos
Text-to-image (T2I) models have recently gained widespread adoption. This has spurred concerns about safeguarding intellectual property rights and an increasing demand for mechanis…
Editable Image Elements for Controllable Synthesis
Jiteng Mu, Michaël Gharbi, Richard Zhang +4
Diffusion models have made significant advances in text-guided synthesis tasks. However, editing user-provided images remains challenging, as the high dimensional noise input space…
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
Qing Liu, Adam Kortylewski, Zhishuai Zhang +7
Part segmentations provide a rich and detailed part-level description of objects. However, their annotation requires an enormous amount of work, which makes it difficult to apply s…
EditAR: Unified Conditional Generation with Autoregressive Models
Jiteng Mu, Nuno Vasconcelos, Xiaolong Wang
Recent progress in controllable image generation and editing is largely driven by diffusion-based methods. Although diffusion models perform exceptionally well in specific tasks wi…
ActorsNeRF: Animatable Few-shot Human Rendering with Generalizable NeRFs
Jiteng Mu, Shen Sang, Nuno Vasconcelos +1
While NeRF-based human representations have shown impressive novel view synthesis results, most methods still rely on a large number of images / views for training. In this work, w…
CoordGAN: Self-Supervised Dense Correspondences Emerge from GANs
Jiteng Mu, Shalini De Mello, Zhiding Yu +4
Recent advances show that Generative Adversarial Networks (GANs) can synthesize images with smooth variations along semantically meaningful latent directions, such as pose, express…
A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation
Jiteng Mu, Weichao Qiu, Adam Kortylewski +3
Recent work has made significant progress on using implicit functions, as a continuous representation for 3D rigid object shape reconstruction. However, much less effort has been d…
Learning Part Segmentation from Synthetic Animals
Jiawei Peng, Ju He, Prakhar Kaushik +3
Semantic part segmentation provides an intricate and interpretable understanding of an object, thereby benefiting numerous downstream tasks. However, the need for exhaustive annota…
Learning Generalizable Feature Fields for Mobile Manipulation
Ri-Zhao Qiu, Yafei Hu, Yuchen Song +8
An open problem in mobile manipulation is how to represent objects and scenes in a unified manner so that robots can use both for navigation and manipulation. The latter requires c…
Learning from Synthetic Animals
Jiteng Mu, Weichao Qiu, Gregory Hager +1
Despite great success in human parsing, progress for parsing other deformable articulated objects, like animals, is still limited by the lack of labeled data. In this paper, we use…