5 papers
Love Me, Love My Label: Rethinking the Role of Labels in Prompt Retrieval for Visual In-Context Learning
Tianci Luo, Haohao Pan, Jinpeng Wang +5
Visual in-context learning (VICL) enables visual foundation models to handle multiple tasks by steering them with demonstrative prompts. The choice of such prompts largely influenc…
CtrlVDiff: Controllable Video Generation via Unified Multimodal Video Diffusion
Dianbing Xi, Jiepeng Wang, Yuanzhi Liang +8
We tackle the dual challenges of video understanding and controllable video generation within a unified diffusion framework. Our key insights are two-fold: geometry-only cues (e.g.…
LTM3D: Bridging Token Spaces for Conditional 3D Generation with Auto-Regressive Diffusion Framework
Xin Kang, Zihan Zheng, Lei Chu +5
We present LTM3D, a Latent Token space Modeling framework for conditional 3D shape generation that integrates the strengths of diffusion and auto-regressive (AR) models. While diff…
ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models
Rui Xu, Jiepeng Wang, Hao Pan +6
In this paper, we study an under-explored but important factor of diffusion generative models, i.e., the combinatorial complexity. Data samples are generally high-dimensional, and…
MVD: Efficient Multiview 3D Reconstruction for Multiview Diffusion
Xin-Yang Zheng, Hao Pan, Yu-Xiao Guo +2
As a promising 3D generation technique, multiview diffusion (MVD) has received a lot of attention due to its advantages in terms of generalizability, quality, and efficiency. By fi…