5 papers
3D-free meets 3D priors: Novel View Synthesis from a Single Image with Pretrained Diffusion Guidance
Taewon Kang, Divya Kothandaraman, Dinesh Manocha +1
Recent 3D novel view synthesis (NVS) methods often require extensive 3D data for training, and also typically lack generalization beyond the training distribution. Moreover, they t…
Financial Models in Generative Art: Black-Scholes-Inspired Concept Blending in Text-to-Image Diffusion
Divya Kothandaraman, Ming Lin, Dinesh Manocha
We introduce a novel approach for concept blending in pretrained text-to-image diffusion models, aiming to generate images at the intersection of multiple text prompts. At each tim…
ImPoster: Text and Frequency Guidance for Subject Driven Action Personalization using Diffusion Models
Divya Kothandaraman, Kuldeep Kulkarni, Sumit Shekhar +2
We present ImPoster, a novel algorithm for generating a target image of a 'source' subject performing a 'driving' action. The inputs to our algorithm are a single pair of a source…
HawkI: Homography & Mutual Information Guidance for 3D-free Single Image to Aerial View
Divya Kothandaraman, Tianyi Zhou, Ming Lin +1
We present HawkI, for synthesizing aerial-view images from text and an exemplar image, without any additional multi-view or 3D information for finetuning or at inference. HawkI use…
Text Prompting for Multi-Concept Video Customization by Autoregressive Generation
Divya Kothandaraman, Kihyuk Sohn, Ruben Villegas +3
We present a method for multi-concept customization of pretrained text-to-video (T2V) models. Intuitively, the multi-concept customized video can be derived from the (non-linear) i…