most citedMALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation

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cs.CV2025

FontAdapter: Instant Font Adaptation in Visual Text Generation

Myungkyu Koo, Subin Kim, Sangkyung Kwak +3

Text-to-image diffusion models have significantly improved the seamless integration of visual text into diverse image contexts. Recent approaches further improve control over font…

cs.CV2025★ 1 cited

MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation

Sihyun Yu, Meera Hahn, Dan Kondratyuk +6

Diffusion models are successful for synthesizing high-quality videos but are limited to generating short clips (e.g., 2-10 seconds). Synthesizing sustained footage (e.g. over minut…

cs.CV2024

Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified Robustness

Suhyeok Jang, Seojin Kim, Jinwoo Shin +1

The remarkable advances in deep learning have led to the emergence of many off-the-shelf classifiers, e.g., large pre-trained models. However, since they are typically trained on c…

cs.CV2024

Controllable Human Image Generation with Personalized Multi-Garments

Yisol Choi, Sangkyung Kwak, Sihyun Yu +2

We present BootComp, a novel framework based on text-to-image diffusion models for controllable human image generation with multiple reference garments. Here, the main bottleneck i…

cs.CV2024

Efficient Long Video Tokenization via Coordinate-based Patch Reconstruction

Huiwon Jang, Sihyun Yu, Jinwoo Shin +2

Efficient tokenization of videos remains a challenge in training vision models that can process long videos. One promising direction is to develop a tokenizer that can encode long…

cs.CV2024★ 1 cited

Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition

Sihyun Yu, Weili Nie, De-An Huang +3

Video diffusion models have recently made great progress in generation quality, but are still limited by the high memory and computational requirements. This is because current vid…