1 citations · 1 across the 11 of their papers we have counts for
11 papers
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…
PRISM: A Unified Framework for Photorealistic Reconstruction and Intrinsic Scene Modeling
Alara Dirik, Tuanfeng Wang, Duygu Ceylan +2
We present PRISM, a unified framework that enables multiple image generation and editing tasks in a single foundational model. Starting from a pre-trained text-to-image diffusion m…
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…
Free-viewpoint Human Animation with Pose-correlated Reference Selection
Fa-Ting Hong, Zhan Xu, Haiyang Liu +6
Diffusion-based human animation aims to animate a human character based on a source human image as well as driving signals such as a sequence of poses. Leveraging the generative ca…
Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation
Hyeonho Jeong, Chun-Hao Paul Huang, Jong Chul Ye +2
While recent foundational video generators produce visually rich output, they still struggle with appearance drift, where objects gradually degrade or change inconsistently across…
FloAt: Flow Warping of Self-Attention for Clothing Animation Generation
Swasti Shreya Mishra, Kuldeep Kulkarni, Duygu Ceylan +1
We propose a diffusion model-based approach, FloAtControlNet to generate cinemagraphs composed of animations of human clothing. We focus on human clothing like dresses, skirts and…
Boosting Camera Motion Control for Video Diffusion Transformers
Soon Yau Cheong, Duygu Ceylan, Armin Mustafa +2
Recent advancements in diffusion models have significantly enhanced the quality of video generation. However, fine-grained control over camera pose remains a challenge. While U-Net…