10 papers · 1 filter
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…
VideoHandles: Editing 3D Object Compositions in Videos Using Video Generative Priors
Juil Koo, Paul Guerrero, Chun-Hao Paul Huang +2
Generative methods for image and video editing use generative models as priors to perform edits despite incomplete information, such as changing the composition of 3D objects shown…
JOG3R: Towards 3D-Consistent Video Generators
Chun-Hao Paul Huang, Niloy Mitra, Hyeonho Jeong +2
Emergent capabilities of image generators have led to many impactful zero- or few-shot applications. Inspired by this success, we investigate whether video generators similarly exh…