145 citations · 353 across the 59 of their papers we have counts for
68 papers
CineOrchestra: Unified Entity-Centric Conditioning for Cinematic Video Generation
Sharath Girish, Tsai-Shien Chen, Zhikang Dong +4
Cinematic video depicts multiple subjects acting or interacting at specific moments, captured with deliberate camera movement, and stitched together by shot transitions. Together,…
ActionParty: Multi-Subject Action Binding in Generative Video Games
Alexander Pondaven, Ziyi Wu, Igor Gilitschenski +4
Recent advances in video diffusion have enabled the development of "world models" capable of simulating interactive environments. However, these models are largely restricted to si…
One Model, Many Budgets: Elastic Latent Interfaces for Diffusion Transformers
Moayed Haji-Ali, Willi Menapace, Ivan Skorokhodov +6
Diffusion transformers (DiTs) achieve high generative quality but lock FLOPs to image resolution, limiting principled latency-quality trade-offs, and allocate computation uniformly…
OmniView: An All-Seeing Diffusion Model for 3D and 4D View Synthesis
Xiang Fan, Sharath Girish, Vivek Ramanujan +6
Prior approaches injecting camera control into diffusion models have focused on specific subsets of 4D consistency tasks: novel view synthesis, text-to-video with camera control, i…
S2DiT: Sandwich Diffusion Transformer for Mobile Streaming Video Generation
Lin Zhao, Yushu Wu, Aleksei Lebedev +11
Diffusion Transformers (DiTs) have recently improved video generation quality. However, their heavy computational cost makes real-time or on-device generation infeasible. In this w…
AlcheMinT: Fine-grained Temporal Control for Multi-Reference Consistent Video Generation
Sharath Girish, Viacheslav Ivanov, Tsai-Shien Chen +3
Recent advances in subject-driven video generation with large diffusion models have enabled personalized content synthesis conditioned on user-provided subjects. However, existing…
EgoEdit: Dataset, Real-Time Streaming Model, and Benchmark for Egocentric Video Editing
Runjia Li, Moayed Haji-Ali, Ashkan Mirzaei +9
We study instruction-guided editing of egocentric videos for interactive AR applications. While recent AI video editors perform well on third-person footage, egocentric views prese…
Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization
Tsai-Shien Chen, Aliaksandr Siarohin, Gordon Guocheng Qian +9
Visual concept personalization aims to transfer only specific image attributes, such as identity, expression, lighting, and style, into unseen contexts. However, existing methods r…
AlphaFlow: Understanding and Improving MeanFlow Models
Huijie Zhang, Aliaksandr Siarohin, Willi Menapace +4
MeanFlow has recently emerged as a powerful framework for few-step generative modeling trained from scratch, but its success is not yet fully understood. In this work, we show that…
Sprint: Sparse-Dense Residual Fusion for Efficient Diffusion Transformers
Dogyun Park, Moayed Haji-Ali, Yanyu Li +5
Diffusion Transformers (DiTs) deliver state-of-the-art generative performance but their quadratic training cost with sequence length makes large-scale pretraining prohibitively exp…
Zero-Shot Dynamic Concept Personalization with Grid-Based LoRA
Rameen Abdal, Or Patashnik, Ekaterina Deyneka +5
Recent advances in text-to-video generation have enabled high-quality synthesis from text and image prompts. While the personalization of dynamic concepts, which capture subject-sp…
Taming Diffusion Transformer for Efficient Mobile Video Generation in Seconds
Yushu Wu, Yanyu Li, Anil Kag +9
Diffusion Transformers (DiT) have shown strong performance in video generation tasks, but their high computational cost makes them impractical for resource-constrained devices like…
Improving Progressive Generation with Decomposable Flow Matching
Moayed Haji-Ali, Willi Menapace, Ivan Skorokhodov +4
Generating high-dimensional visual modalities is a computationally intensive task. A common solution is progressive generation, where the outputs are synthesized in a coarse-to-fin…
4Real-Video-V2: Fused View-Time Attention and Feedforward Reconstruction for 4D Scene Generation
Chaoyang Wang, Ashkan Mirzaei, Vidit Goel +9
We propose the first framework capable of computing a 4D spatio-temporal grid of video frames and 3D Gaussian particles for each time step using a feed-forward architecture. Our ar…
DenseDPO: Fine-Grained Temporal Preference Optimization for Video Diffusion Models
Ziyi Wu, Anil Kag, Ivan Skorokhodov +5
Direct Preference Optimization (DPO) has recently been applied as a post-training technique for text-to-video diffusion models. To obtain training data, annotators are asked to pro…
H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models
Yushu Wu, Yanyu Li, Ivan Skorokhodov +6
Autoencoder (AE) is the key to the success of latent diffusion models for image and video generation, reducing the denoising resolution and improving efficiency. However, the power…
Dynamic Concepts Personalization from Single Videos
Rameen Abdal, Or Patashnik, Ivan Skorokhodov +5
Personalizing generative text-to-image models has seen remarkable progress, but extending this personalization to text-to-video models presents unique challenges. Unlike static con…
Improving the Diffusability of Autoencoders
Ivan Skorokhodov, Sharath Girish, Benran Hu +5
Latent diffusion models have emerged as the leading approach for generating high-quality images and videos, utilizing compressed latent representations to reduce the computational…
Multi-subject Open-set Personalization in Video Generation
Tsai-Shien Chen, Aliaksandr Siarohin, Willi Menapace +7
Video personalization methods allow us to synthesize videos with specific concepts such as people, pets, and places. However, existing methods often focus on limited domains, requi…
AV-Link: Temporally-Aligned Diffusion Features for Cross-Modal Audio-Video Generation
Moayed Haji-Ali, Willi Menapace, Aliaksandr Siarohin +5
We propose AV-Link, a unified framework for Video-to-Audio (A2V) and Audio-to-Video (A2V) generation that leverages the activations of frozen video and audio diffusion models for t…
Video Motion Transfer with Diffusion Transformers
Alexander Pondaven, Aliaksandr Siarohin, Sergey Tulyakov +2
We propose DiTFlow, a method for transferring the motion of a reference video to a newly synthesized one, designed specifically for Diffusion Transformers (DiT). We first process t…
Mind the Time: Temporally-Controlled Multi-Event Video Generation
Ziyi Wu, Aliaksandr Siarohin, Willi Menapace +5
Real-world videos consist of sequences of events. Generating such sequences with precise temporal control is infeasible with existing video generators that rely on a single paragra…
AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation
Anil Kag, Huseyin Coskun, Jierun Chen +5
Neural network architecture design requires making many crucial decisions. The common desiderata is that similar decisions, with little modifications, can be reused in a variety of…
AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers
Sherwin Bahmani, Ivan Skorokhodov, Guocheng Qian +5
Numerous works have recently integrated 3D camera control into foundational text-to-video models, but the resulting camera control is often imprecise, and video generation quality…