3 citations · 6 across the 14 of their papers we have counts for
6 papers · 1 filter
Representation Alignment for Just Image Transformers is not Easier than You Think
Jaeyo Shin, Jiwook Kim, Hyunjung Shim
Representation Alignment (REPA) has emerged as a simple way to accelerate Diffusion Transformers training in latent space. At the same time, pixel-space diffusion transformers such…
Improving Video Diffusion Transformer Training by Multi-Feature Fusion and Alignment from Self-Supervised Vision Encoders
Dohun Lee, Hyeonho Jeong, Jiwook Kim +2
Video diffusion models have advanced rapidly in the recent years as a result of series of architectural innovations (e.g., diffusion transformers) and use of novel training objecti…
3D-Aware Vision-Language Models Fine-Tuning with Geometric Distillation
Seonho Lee, Jiho Choi, Inha Kang +3
Vision-Language Models (VLMs) have shown remarkable performance on diverse visual and linguistic tasks, yet they remain fundamentally limited in their understanding of 3D spatial s…
Scribble-Guided Diffusion for Training-free Text-to-Image Generation
Seonho Lee, Jiho Choi, Seohyun Lim +2
Recent advancements in text-to-image diffusion models have demonstrated remarkable success, yet they often struggle to fully capture the user's intent. Existing approaches using te…
DreamCatalyst: Fast and High-Quality 3D Editing via Controlling Editability and Identity Preservation
Jiwook Kim, Seonho Lee, Jaeyo Shin +2
Score distillation sampling (SDS) has emerged as an effective framework in text-driven 3D editing tasks, leveraging diffusion models for 3D-consistent editing. However, existing SD…
Class-Continuous Conditional Generative Neural Radiance Field
Jiwook Kim, Minhyeok Lee
The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been…