8 papers
Error-Conditioned Neural Solvers
Haina Jiang, Liam Wang, Peng-Chen Chen +4
Neural surrogate models offer fast approximate mappings from PDE parameters to solutions, but they typically treat solving as a purely statistical task: once trained, they struggle…
Better Source, Better Flow: Learning Condition-Dependent Source Distribution for Flow Matching
Junwan Kim, Jiho Park, Seonghu Jeon +1
Flow matching has recently emerged as a promising alternative to diffusion-based generative models, particularly for text-to-image generation. Despite its flexibility in allowing a…
Exploring Conditions for Diffusion models in Robotic Control
Heeseong Shin, Byeongho Heo, Dongyoon Han +2
While pre-trained visual representations have significantly advanced imitation learning, they are often task-agnostic as they remain frozen during policy learning. In this work, we…
CORAL: Correspondence Alignment for Improved Virtual Try-On
Jiyoung Kim, Youngjin Shin, Siyoon Jin +6
Existing methods for Virtual Try-On (VTON) often struggle to preserve fine garment details, especially in unpaired settings where accurate person-garment correspondence is required…
Projected Representation Conditioning for High-fidelity Novel View Synthesis
Min-Seop Kwak, Minkyung Kwon, Jinhyeok Choi +2
We propose a novel framework for diffusion-based novel view synthesis in which we leverage external representations as conditions, harnessing their geometric and semantic correspon…
Aligned Novel View Image and Geometry Synthesis via Cross-modal Attention Instillation
Min-Seop Kwak, Junho Kim, Sangdoo Yun +4
We introduce a diffusion-based framework that performs aligned novel view image and geometry generation via a warping-and-inpainting methodology. Unlike prior methods that require…