7 papers
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…
BézierFlow: Learning Bézier Stochastic Interpolant Schedulers for Few-Step Generation
Yunhong Min, Juil Koo, Seungwoo Yoo +1
We introduce BézierFlow, a lightweight training approach for few-step generation with pretrained diffusion and flow models. BézierFlow achieves a 2-3x performance improvement for…
Token Warping Helps MLLMs Look from Nearby Viewpoints
Phillip Y. Lee, Chanho Park, Mingue Park +3
Can warping tokens, rather than pixels, help multimodal large language models (MLLMs) understand how a scene appears from a nearby viewpoint? While MLLMs perform well on visual rea…
DiffusionRollout: Uncertainty-Aware Rollout Planning in Long-Horizon PDE Solving
Seungwoo Yoo, Juil Koo, Daehyeon Choi +1
We propose DiffusionRollout, a novel selective rollout planning strategy for autoregressive diffusion models, aimed at mitigating error accumulation in long-horizon predictions of…
Proxy-Free Gaussian Splats Deformation with Splat-Based Surface Estimation
Jaeyeong Kim, Seungwoo Yoo, Minhyuk Sung
We introduce SpLap, a proxy-free deformation method for Gaussian splats (GS) based on a Laplacian operator computed from our novel surface-aware splat graph. Existing approaches to…
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…