13 citations · 31 across the 8 of their papers we have counts for
8 papers
Fashion-VDM: Video Diffusion Model for Virtual Try-On
Johanna Karras, Yingwei Li, Nan Liu +5
We present Fashion-VDM, a video diffusion model (VDM) for generating virtual try-on videos. Given an input garment image and person video, our method aims to generate a high-qualit…
Compositional Image Decomposition with Diffusion Models
Jocelin Su, Nan Liu, Yanbo Wang +2
Given an image of a natural scene, we are able to quickly decompose it into a set of components such as objects, lighting, shadows, and foreground. We can then envision a scene whe…
M&M VTO: Multi-Garment Virtual Try-On and Editing
Luyang Zhu, Yingwei Li, Nan Liu +3
We present M&M VTO, a mix and match virtual try-on method that takes as input multiple garment images, text description for garment layout and an image of a person. An example inpu…
Unsupervised Compositional Concepts Discovery with Text-to-Image Generative Models
Nan Liu, Yilun Du, Shuang Li +2
Text-to-image generative models have enabled high-resolution image synthesis across different domains, but require users to specify the content they wish to generate. In this paper…
Compositional Visual Generation with Composable Diffusion Models
Nan Liu, Shuang Li, Yilun Du +2
Large text-guided diffusion models, such as DALLE-2, are able to generate stunning photorealistic images given natural language descriptions. While such models are highly flexible,…
Learning to Compose Visual Relations
Nan Liu, Shuang Li, Yilun Du +2
The visual world around us can be described as a structured set of objects and their associated relations. An image of a room may be conjured given only the description of the unde…
Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models
Won Park, Nan Liu, Qi Alfred Chen +1
A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models…
FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework
Santiago Castro, Ruoyao Wang, Pingxuan Huang +5
We propose fill-in-the-blanks as a video understanding evaluation framework and introduce FIBER -- a novel dataset consisting of 28,000 videos and descriptions in support of this e…