4 citations · 11 across the 17 of their papers we have counts for
10 papers · 1 filter
JUICE: Unified Multimodal Modeling in Pretrained Representation Spaces
Jiahan Li, Huangyuan Su, Shengbang Tong +9
Most unified multimodal models treat images and text asymmetrically: a pretrained language model generates text, an attached diffusion head generates images, and outputs that conta…
Purify Before You Align: Improving Representation Alignment for Diffusion Models
Yingheng Wang, Yaoqiang Li, Yaqin Wu +4
Representation alignment improves and accelerates diffusion training by encouraging intermediate features to match a pretrained visual encoder. However, standard alignment matches…
DenseAnnotate: Enabling Scalable Dense Caption Collection for Images and 3D Scenes via Spoken Descriptions
Xiaoyu Lin, Aniket Ghorpade, Hansheng Zhu +9
With the rapid adoption of multimodal large language models (MLLMs) across diverse applications, there is a pressing need for task-centered, high-quality training data. A key limit…
World-consistent Video Diffusion with Explicit 3D Modeling
Qihang Zhang, Shuangfei Zhai, Miguel Angel Bautista +4
Recent advancements in diffusion models have set new benchmarks in image and video generation, enabling realistic visual synthesis across single- and multi-frame contexts. However,…
Normalizing Flows are Capable Generative Models
Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran +7
Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but ha…
TypeScore: A Text Fidelity Metric for Text-to-Image Generative Models
Georgia Gabriela Sampaio, Ruixiang Zhang, Shuangfei Zhai +4
Evaluating text-to-image generative models remains a challenge, despite the remarkable progress being made in their overall performances. While existing metrics like CLIPScore work…
DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation
Jiatao Gu, Yuyang Wang, Yizhe Zhang +5
Diffusion models have become the dominant approach for visual generation. They are trained by denoising a Markovian process which gradually adds noise to the input. We argue that t…
Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling
Jiatao Gu, Ying Shen, Shuangfei Zhai +3
Diffusion models have emerged as a powerful tool for generating high-quality images from textual descriptions. Despite their successes, these models often exhibit limited diversity…
Many-to-many Image Generation with Auto-regressive Diffusion Models
Ying Shen, Yizhe Zhang, Shuangfei Zhai +3
Recent advancements in image generation have made significant progress, yet existing models present limitations in perceiving and generating an arbitrary number of interrelated ima…
Efficient-NeRF2NeRF: Streamlining Text-Driven 3D Editing with Multiview Correspondence-Enhanced Diffusion Models
Liangchen Song, Liangliang Cao, Jiatao Gu +3
The advancement of text-driven 3D content editing has been blessed by the progress from 2D generative diffusion models. However, a major obstacle hindering the widespread adoption…