1 citations · 1 across the 12 of their papers we have counts for
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Geometric 4D Stitching for Grounded 4D Generation
Sunwoo Park, Taesung Kwon, Jong Chul Ye
Recent 4D generation methods complete scene-level missing information using generative models and reconstruct the scene into radiance-based representations. However, these pipeline…
CRePE: Curved Ray Expectation Positional Encoding for Unified-Camera-Controlled Video Generation
Seonghyun Jin, Youngmin Kim, Sunwoo Park +1
Video world models should predict future appearance in a way that remains consistent with 3D scene structure, camera motion, and lens geometry. Existing attention-level camera enco…
MotionCFG: Boosting Motion Dynamics via Stochastic Concept Perturbation
Byungjun Kim, Soobin Um, Jong Chul Ye
Despite recent advances in Text-to-Video (T2V) synthesis, generating high-fidelity and dynamic motion remains a significant challenge. Existing methods primarily rely on Classifier…
Align Your Tangent: Training Better Consistency Models via Manifold-Aligned Tangents
Beomsu Kim, Byunghee Cha, Jong Chul Ye
With diffusion and flow matching models achieving state-of-the-art generating performance, the interest of the community now turned to reducing the inference time without sacrifici…
Minority-Focused Text-to-Image Generation via Prompt Optimization
Soobin Um, Jong Chul Ye
We investigate the generation of minority samples using pretrained text-to-image (T2I) latent diffusion models. Minority instances, in the context of T2I generation, can be defined…
Self-Guided Generation of Minority Samples Using Diffusion Models
Soobin Um, Jong Chul Ye
We present a novel approach for generating minority samples that live on low-density regions of a data manifold. Our framework is built upon diffusion models, leveraging the princi…