12 papers · 1 filter
Auteur: Language-Driven Cinematographic Framing for Human-Centric Video Generation
Muhammed Burak Kizil, Enes Sanli, Niloy J. Mitra +4
Generative video models have achieved remarkable visual fidelity and temporal coherence, yet intentional camera control remains elusive. Existing frameworks treat camera motion as…
Memory-V2V: Memory-Augmented Video-to-Video Diffusion for Consistent Multi-Turn Editing
Dohun Lee, Chun-Hao Paul Huang, Xuelin Chen +3
Video-to-video diffusion models achieve impressive single-turn editing performance, but practical editing workflows are inherently iterative. When edits are applied sequentially, e…
V-RGBX: Video Editing with Accurate Controls over Intrinsic Properties
Ye Fang, Tong Wu, Valentin Deschaintre +6
Large-scale video generation models have shown remarkable potential in modeling photorealistic appearance and lighting interactions in real-world scenes. However, a closed-loop fra…
ReasonX: MLLM-Guided Intrinsic Image Decomposition
Alara Dirik, Tuanfeng Wang, Duygu Ceylan +2
Intrinsic image decomposition aims to separate images into physical components such as albedo, depth, normals, and illumination. While recent diffusion- and transformer-based model…
Improving Video Diffusion Transformer Training by Multi-Feature Fusion and Alignment from Self-Supervised Vision Encoders
Dohun Lee, Hyeonho Jeong, Jiwook Kim +2
Video diffusion models have advanced rapidly in the recent years as a result of series of architectural innovations (e.g., diffusion transformers) and use of novel training objecti…
ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion Models
Ozgur Kara, Krishna Kumar Singh, Feng Liu +3
Current diffusion-based text-to-video methods are limited to producing short video clips of a single shot and lack the capability to generate multi-shot videos with discrete transi…