9 papers
MUNI: Multimodal Unified Latent Diffusion for Coherent Any-to-Any Generation
Kyeongmin Yeo, Yunhong Min, Minhyuk Sung
We introduce MUNI, an end-to-end multimodal latent diffusion framework for any-to-any generation that unifies subset-conditioned cross-modal generation and unconditional joint samp…
PairFlow: Closed-Form Source-Target Coupling for Few-Step Generation in Discrete Flow Models
Mingue Park, Jisung Hwang, Seungwoo Yoo +2
We introduce , a lightweight preprocessing step for training Discrete Flow Models (DFMs) to achieve few-step sampling without requiring a pretrained teacher. DFM…
MatLat: Material Latent Space for PBR Texture Generation
Kyeongmin Yeo, Yunhong Min, Jaihoon Kim +1
We propose a generative framework for producing high-quality PBR textures on a given 3D mesh. As large-scale PBR texture datasets are scarce, our approach focuses on effectively le…
Neural Green's Functions
Seungwoo Yoo, Kyeongmin Yeo, Jisung Hwang +1
We introduce Neural Green's Function, a neural solution operator for linear partial differential equations (PDEs) whose differential operators admit eigendecompositions. Inspired b…
Psi-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models
Taehoon Yoon, Yunhong Min, Kyeongmin Yeo +1
We introduce -Sampler, an SMC-based framework incorporating pCNL-based initial particle sampling for effective inference-time reward alignment with a score-based generative mod…
ORIGEN: Zero-Shot 3D Orientation Grounding in Text-to-Image Generation
Yunhong Min, Daehyeon Choi, Kyeongmin Yeo +2
We introduce ORIGEN, the first zero-shot method for 3D orientation grounding in text-to-image generation across multiple objects and diverse categories. While previous work on spat…