7 papers
Energy-Guided Flow Matching
Haoyang Tong, Yu He, Fang Li +6
Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dimensional space. Standard flo…
HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion
Yu He, Lichen Ma, Zipeng Guo +5
Pixel-space diffusion models bypass the reconstruction bottleneck of Variational Autoencoders (VAEs) but face a fundamental "granularity dilemma": capturing global semantics favors…
LiWi: Layering in the Wild
Yu He, Fang Li, Haoyang Tong +7
Recent advances in generative models have empowered impressive layered image generation, yet their success is largely confined to graphic design domains. The layering of in-the-wil…
InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation
Yang Yue, Fangyun Wei, Tianyu He +10
Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging for autoregressive generators built on d…
Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos
Qixiu Li, Yu Deng, Yaobo Liang +14
This paper presents a novel approach for pretraining robotic manipulation Vision-Language-Action (VLA) models using a large corpus of unscripted real-life video recordings of human…
Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis
Bowen Zhang, Sicheng Xu, Chuxin Wang +4
In this paper, we present a novel framework for video-to-4D generation that creates high-quality dynamic 3D content from single video inputs. Direct 4D diffusion modeling is extrem…