5 citations · 5 across the 4 of their papers we have counts for
9 papers · 1 filter
Low-Bitrate Video Compression through Semantic-Conditioned Diffusion
Lingdong Wang, Guan-Ming Su, Divya Kothandaraman +3
Traditional video codecs optimized for pixel fidelity collapse at ultra-low bitrates and produce severe artifacts. This failure arises from a fundamental misalignment between pixel…
Zero-Shot Personalized Camera Motion Control for Image-to-Video Synthesis
Pooja Guhan, Divya Kothandaraman, Geonsun Lee +3
Specifying nuanced and compelling camera motion remains a significant hurdle for non-expert creators using generative tools, creating an "expressive gap" where generic text prompts…
Scene-Action Prompt Fusion for Coherent Text-to-Video Storytelling
Taewon Kang, Divya Kothandaraman, Ming C. Lin
Generating coherent long-form video sequences from discrete text prompts remains challenging due to difficulties in maintaining temporal coherence, semantic consistency, and scene-…
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…
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…
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…