6 papers · 1 filter
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…
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…
Structured 3D Latents for Scalable and Versatile 3D Generation
Jianfeng Xiang, Zelong Lv, Sicheng Xu +6
We introduce a novel 3D generation method for versatile and high-quality 3D asset creation. The cornerstone is a unified Structured LATent (SLAT) representation which allows decodi…