6 papers
Mitty: Diffusion-based Human-to-Robot Video Generation
Yiren Song, Cheng Liu, Weijia Mao +1
Learning directly from human demonstration videos is a key milestone toward scalable and generalizable robot learning. Yet existing methods rely on intermediate representations suc…
OmniPSD: Layered PSD Generation with Diffusion Transformer
Cheng Liu, Yiren Song, Haofan Wang +1
Recent advances in diffusion models have greatly improved image generation and editing, yet generating or reconstructing layered PSD files with transparent alpha channels remains h…
Score-based Idempotent Distillation of Diffusion Models
Shehtab Zaman, Chengyan Liu, Kenneth Chiu
Idempotent generative networks (IGNs) are a new line of generative models based on idempotent mapping to a target manifold. IGNs support both single-and multi-step generation, allo…
OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data
Yiren Song, Cheng Liu, Mike Zheng Shou
Diffusion models have advanced image stylization significantly, yet two core challenges persist: (1) maintaining consistent stylization in complex scenes, particularly identity, co…
MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation
Yiren Song, Cheng Liu, Mike Zheng Shou
A hallmark of human intelligence is the ability to create complex artifacts through structured multi-step processes. Generating procedural tutorials with AI is a longstanding but c…
Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training
Shuyin Xia, Xinjun Ma, Zhiyuan Liu +3
Graph Neural Networks (GNNs) have demonstrated significant achievements in processing graph data, yet scalability remains a substantial challenge. To address this, numerous graph c…