4 citations · 7 across the 12 of their papers we have counts for
12 papers
Drifting Field Policy: A One-Step Generative Policy via Wasserstein Gradient Flow
Juil Koo, Mingue Park, Jiwon Choi +2
We propose Drifting Field Policy (DFP), a non-ODE one-step generative policy built on the drifting model paradigm. We frame the policy update as a reverse-KL Wasserstein-2 gradient…
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…
BoxSplitGen: A Generative Model for 3D Part Bounding Boxes in Varying Granularity
Juil Koo, Wei-Tung Lin, Chanho Park +2
Human creativity follows a perceptual process, moving from abstract ideas to finer details during creation. While 3D generative models have advanced dramatically, models specifical…
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…
Toward Ambulatory Vision: Learning Visually-Grounded Active View Selection
Juil Koo, Daehyeon Choi, Sangwoo Youn +2
Vision Language Models (VLMs) excel at visual question answering (VQA) but remain limited to snapshot vision, reasoning from static images. In contrast, embodied agents require amb…
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 s…
VideoHandles: Editing 3D Object Compositions in Videos Using Video Generative Priors
Juil Koo, Paul Guerrero, Chun-Hao Paul Huang +2
Generative methods for image and video editing use generative models as priors to perform edits despite incomplete information, such as changing the composition of 3D objects shown…
Neural Pose Representation Learning for Generating and Transferring Non-Rigid Object Poses
Seungwoo Yoo, Juil Koo, Kyeongmin Yeo +1
We propose a novel method for learning representations of poses for 3D deformable objects, which specializes in 1) disentangling pose information from the object's identity, 2) fac…
SyncTweedies: A General Generative Framework Based on Synchronized Diffusions
Jaihoon Kim, Juil Koo, Kyeongmin Yeo +1
We introduce a general framework for generating diverse visual content, including ambiguous images, panorama images, mesh textures, and Gaussian splat textures, by synchronizing mu…
Posterior Distillation Sampling
Juil Koo, Chanho Park, Minhyuk Sung
We introduce Posterior Distillation Sampling (PDS), a novel optimization method for parametric image editing based on diffusion models. Existing optimization-based methods, which l…
SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulation
Juil Koo, Seungwoo Yoo, Minh Hieu Nguyen +1
We present a cascaded diffusion model based on a part-level implicit 3D representation. Our model achieves state-of-the-art generation quality and also enables part-level shape edi…
PartGlot: Learning Shape Part Segmentation from Language Reference Games
Juil Koo, Ian Huang, Panos Achlioptas +2
We introduce PartGlot, a neural framework and associated architectures for learning semantic part segmentation of 3D shape geometry, based solely on part referential language. We e…