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Hyeonho Jeong

8 accepted papers

2026

SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time

CVPR 2026

We present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently alter both the camera viewpoint and the motion sequence within the generative process, re-rendering the scene for conti

Cited by 0SourcecodeScholar
2025

Reangle-A-Video: 4D Video Generation as Video-to-Video Translation

ICCV 2025poster

We introduce Reangle-A-Video, a unified framework for generating synchronized multi-view videos from a single input video. Unlike mainstream approaches that train multi-view video diffusion models on large-scale 4D datasets, our method reframes the multi-view video generation task as video-to-videos…

2025

Spectral Motion Alignment for Video Motion Transfer Using Diffusion Models

AAAI 2025technical

Diffusion models have significantly facilitated the customization of input video with target appearance while maintaining its motion patterns. To distill the motion information from video frames, existing works often estimate motion representations as frame difference or correlation in pixel-/featur…

Cited by 9SourcePDFScholar
2025

Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation

CVPR 2025poster

While recent foundational video generators produce visually rich output, they still struggle with appearance drift, where objects gradually degrade or change inconsistently across frames, breaking visual coherence. We hypothesize that this is because there is no explicit supervision in terms of spat…

2024

DreamMotion: Space-Time Self-Similar Score Distillation for Zero-Shot Video Editing

ECCV 2024poster

"Text-driven diffusion-based video editing presents a unique challenge not encountered in image editing literature: establishing real-world motion. Unlike existing video editing approaches, here we focus on score distillation sampling to circumvent the standard reverse diffusion process and initiate…

Cited by 7SourcePDFScholar
2024

Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models

ICLR 2024poster

This paper introduces a novel grounding-guided video-to-video translation framework called Ground-A-Video for multi-attribute video editing. Recent endeavors in video editing have showcased promising results in single-attribute editing or style transfer tasks, either by training T2V models on text-v…

2024

Neural Network Training Strategy To Enhance Anomaly Detection Performance: A Perspective On Reconstruction Loss Amplification

ICASSP 2024accepted

Unsupervised anomaly detection (UAD) is a widely adopted approach in industry due to rare anomaly occurrences and data imbalance. A desirable characteristic of an UAD model is contained generalization ability which excels in the reconstruction of seen normal patterns but struggles with unseen anomal…

Cited by 0SourceScholar
2024

VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models

CVPR 2024poster

Text-to-video diffusion models have advanced video generation significantly. However customizing these models to generate videos with tailored motions presents a substantial challenge. In specific they encounter hurdles in (1) accurately reproducing motion from a target video and (2) creating divers…