2 citations · 2 across the 7 of their papers we have counts for
8 papers · 1 filter
Can Video World Models Track Unobserved World States?
Joonghyuk Shin, Yicong Hong, Jaesik Park +1
Video world models are increasingly used as simulators, but visual fidelity alone does not show that a model maintains the hidden state of the world. We examine this difference wit…
Direct Reward Fine-Tuning on Poses for Single Image to 3D Human in the Wild
Seunguk Do, Minwoo Huh, Joonghyuk Shin +1
Single-view 3D human reconstruction has achieved remarkable progress through the adoption of multi-view diffusion models, yet the recovered 3D humans often exhibit unnatural poses.…
SEAL-pose: Enhancing 3D Human Pose Estimation via a Learned Loss for Structural Consistency
Yeonsung Kim, Junggeun Do, Seunguk Do +3
3D human pose estimation (HPE) is characterized by intricate local and global dependencies among joints. Conventional supervised losses are limited in capturing these correlations…
MotionStream: Real-Time Video Generation with Interactive Motion Controls
Joonghyuk Shin, Zhengqi Li, Richard Zhang +4
Current motion-conditioned video generation methods suffer from prohibitive latency (minutes per video) and non-causal processing that prevents real-time interaction. We present Mo…
Exploring Multimodal Diffusion Transformers for Enhanced Prompt-based Image Editing
Joonghyuk Shin, Alchan Hwang, Yujin Kim +2
Transformer-based diffusion models have recently superseded traditional U-Net architectures, with multimodal diffusion transformers (MM-DiT) emerging as the dominant approach in st…
JAM-Flow: Joint Audio-Motion Synthesis with Flow Matching
Mingi Kwon, Joonghyuk Shin, Jaeseok Jung +2
The intrinsic link between facial motion and speech is often overlooked in generative modeling, where talking head synthesis and text-to-speech (TTS) are typically addressed as sep…
ISAC: Training-Free Instance-to-Semantic Attention Control for Multi-Instance Generation
Sanghyun Jo, Wooyeol Lee, Ziseok Lee +3
Recent open-weight text-to-image (T2I) diffusion models still struggle with multi-instance prompts, often omitting or merging instances and mixing semantics among similar objects.…
Distilling Diffusion Models into Conditional GANs
Minguk Kang, Richard Zhang, Connelly Barnes +6
We propose a method to distill a complex multistep diffusion model into a single-step conditional GAN student model, dramatically accelerating inference, while preserving image qua…