27 citations · 58 across the 22 of their papers we have counts for
11 papers · 1 filter
Benchmarking Visual State Tracking in Multimodal Video Understanding
Sihyun Yu, Nanye Ma, Pinzhi Huang +8
Understanding a video requires more than recognizing isolated moments, as humans continuously track entities, states, and events over time. This capacity for visual state tracking…
Decoupled MeanFlow: Turning Flow Models into Flow Maps for Accelerated Sampling
Kyungmin Lee, Sihyun Yu, Jinwoo Shin
Denoising generative models, such as diffusion and flow-based models, produce high-quality samples but require many denoising steps due to discretization error. Flow maps, which es…
Improving Motion in Image-to-Video Models via Adaptive Low-Pass Guidance
June Suk Choi, Kyungmin Lee, Sihyun Yu +3
Recent text-to-video (T2V) models have demonstrated strong capabilities in producing high-quality, dynamic videos. To improve the visual controllability, recent works have consider…
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…
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…
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…
Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
Sihyun Yu, Sangkyung Kwak, Huiwon Jang +4
Recent studies have shown that the denoising process in (generative) diffusion models can induce meaningful (discriminative) representations inside the model, though the quality of…
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…
Video Probabilistic Diffusion Models in Projected Latent Space
Sihyun Yu, Kihyuk Sohn, Subin Kim +1
Despite the remarkable progress in deep generative models, synthesizing high-resolution and temporally coherent videos still remains a challenge due to their high-dimensionality an…
Scalable Neural Video Representations with Learnable Positional Features
Subin Kim, Sihyun Yu, Jaeho Lee +1
Succinct representation of complex signals using coordinate-based neural representations (CNRs) has seen great progress, and several recent efforts focus on extending them for hand…
Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks
Sihyun Yu, Jihoon Tack, Sangwoo Mo +4
In the deep learning era, long video generation of high-quality still remains challenging due to the spatio-temporal complexity and continuity of videos. Existing prior works have…