11 papers
Understanding, Accelerating, and Improving MeanFlow Training
Jin-Young Kim, Hyojun Go, Lea Bogensperger +5
MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields. Yet, the underlying training dynamics remain unclear…
Stitched Value Model for Diffusion Alignment
Hyojun Go, Hyungjin Chung, Prune Truong +8
For practical use, diffusion- or flow-based generative models must be aligned with task-specific rewards, such as prompt fidelity or aesthetic preference. That alignment is challen…
Video Parallel Scaling: Aggregating Diverse Frame Subsets for VideoLLMs
Hyungjin Chung, Hyelin Nam, Jiyeon Kim +6
Video Large Language Models (VideoLLMs) face a critical bottleneck: increasing the number of input frames to capture fine-grained temporal detail leads to prohibitive computational…
Text-to-3D by Stitching a Multi-view Reconstruction Network to a Video Generator
Hyojun Go, Dominik Narnhofer, Goutam Bhat +3
The rapid progress of large, pretrained models for both visual content generation and 3D reconstruction opens up new possibilities for text-to-3D generation. Intuitively, one could…
Generating Human Motion Videos using a Cascaded Text-to-Video Framework
Hyelin Nam, Hyojun Go, Byeongjun Park +2
Human video generation is becoming an increasingly important task with broad applications in graphics, entertainment, and embodied AI. Despite the rapid progress of video diffusion…
SteerX: Creating Any Camera-Free 3D and 4D Scenes with Geometric Steering
Byeongjun Park, Hyojun Go, Hyelin Nam +3
Recent progress in 3D/4D scene generation emphasizes the importance of physical alignment throughout video generation and scene reconstruction. However, existing methods improve th…