10 papers · 1 filter
InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem
Yeobin Hong, Suhyeon Lee, Hyungjin Chung +1
Recent approaches in controllable novel view video generation often rely on fine-tuning pre-trained Video Diffusion Models (VDMs). This dominant paradigm is computationally expensi…
Training-Free Reward-Guided Image Editing via Trajectory Optimal Control
Jinho Chang, Jaemin Kim, Jong Chul Ye
Recent advancements in diffusion and flow-matching models have demonstrated remarkable capabilities in high-fidelity image synthesis. A prominent line of research involves reward-g…
ReDirector: Creating Any-Length Video Retakes with Rotary Camera Encoding
Byeongjun Park, Byung-Hoon Kim, Hyungjin Chung +1
We present ReDirector, a novel camera-controlled video retake generation method for dynamically captured variable-length videos. In particular, we rectify a common misuse of RoPE i…
InvFusion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems
Noam Elata, Hyungjin Chung, Jong Chul Ye +2
Diffusion Models have demonstrated remarkable capabilities in handling inverse problems, offering high-quality posterior-sampling-based solutions. Despite significant advances, a f…
FreeGuide: Training-Free Text-to-Video Alignment using Image LVLM
Jaemin Kim, Bryan Sangwoo Kim, Jong Chul Ye
Diffusion models have achieved impressive results in generative tasks for text-to-video (T2V) synthesis. However, achieving accurate text alignment in T2V generation remains challe…
Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAI
Won Jun Kim, Hyungjin Chung, Jaemin Kim +3
Gradient-based methods are a prototypical family of explainability techniques, especially for image-based models. Nonetheless, they have several shortcomings in that they (1) requi…