22 citations · 32 across the 10 of their papers we have counts for
10 papers
WAM-Diff2: Hierarchical AR-to-Diffusion Distillation for Highly Efficient Autonomous Driving VLA
Zhihao Zhu, Hanlin Shang, Mingwang Xu +6
Vision-Language-Action (VLA) models have emerged as a prominent paradigm for end-to-end autonomous driving; however, their efficient deployment is severely constrained by high comp…
EasyControl: Transfer ControlNet to Video Diffusion for Controllable Generation and Interpolation
Cong Wang, Jiaxi Gu, Panwen Hu +5
Following the advancements in text-guided image generation technology exemplified by Stable Diffusion, video generation is gaining increased attention in the academic community. Ho…
Self-Adaptive Reality-Guided Diffusion for Artifact-Free Super-Resolution
Qingping Zheng, Ling Zheng, Yuanfan Guo +4
Artifact-free super-resolution (SR) aims to translate low-resolution images into their high-resolution counterparts with a strict integrity of the original content, eliminating any…
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion
Guansong Lu, Yuanfan Guo, Jianhua Han +7
Current large-scale diffusion models represent a giant leap forward in conditional image synthesis, capable of interpreting diverse cues like text, human poses, and edges. However,…
DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidance
Cong Wang, Jiaxi Gu, Panwen Hu +3
Image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods try to extend pre-trained text-guided i…
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models
Fengyuan Shi, Jiaxi Gu, Hang Xu +3
Diffusion models have made tremendous progress in text-driven image and video generation. Now text-to-image foundation models are widely applied to various downstream image synthes…
Fuse Your Latents: Video Editing with Multi-source Latent Diffusion Models
Tianyi Lu, Xing Zhang, Jiaxi Gu +5
Latent Diffusion Models (LDMs) are renowned for their powerful capabilities in image and video synthesis. Yet, compared to text-to-image (T2I) editing, text-to-video (T2V) editing…
Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images
Qingping Zheng, Yuanfan Guo, Jiankang Deng +4
Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This is…
Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation
Jiaxi Gu, Shicong Wang, Haoyu Zhao +7
Inspired by the remarkable success of Latent Diffusion Models (LDMs) for image synthesis, we study LDM for text-to-video generation, which is a formidable challenge due to the comp…
HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving
Xinpeng Ding, Jianhua Han, Hang Xu +2
Recent efforts to use natural language for interpretable driving focus mainly on planning, neglecting perception tasks. In this paper, we address this gap by introducing ROLISP (Ri…