1 citations · 4 across the 49 of their papers we have counts for
25 papers · 1 filter
EasyV2V: A High-quality Instruction-based Video Editing Framework
Jinjie Mai, Chaoyang Wang, Guocheng Gordon Qian +5
While image editing has advanced rapidly, video editing remains less explored, facing challenges in consistency, control, and generalization. We study the design space of data, arc…
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…
Canvas-to-Image: Compositional Image Generation with Multimodal Controls
Yusuf Dalva, Guocheng Gordon Qian, Maya Goldenberg +5
While modern diffusion models excel at generating high-quality and diverse images, they still struggle with high-fidelity compositional and multimodal control, particularly when us…
Preventing Shortcuts in Adapter Training via Providing the Shortcuts
Anujraaj Argo Goyal, Guocheng Gordon Qian, Huseyin Coskun +8
Adapter-based training has emerged as a key mechanism for extending the capabilities of powerful foundation image generators, enabling personalized and stylized text-to-image synth…
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…
ComposeMe: Attribute-Specific Image Prompts for Controllable Human Image Generation
Guocheng Gordon Qian, Daniil Ostashev, Egor Nemchinov +4
Generating high-fidelity images of humans with fine-grained control over attributes such as hairstyle and clothing remains a core challenge in personalized text-to-image synthesis.…
ShapeGen4D: Towards High Quality 4D Shape Generation from Videos
Jiraphon Yenphraphai, Ashkan Mirzaei, Jianqi Chen +5
Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. In this work, we introduce a native vide…
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…
CanvasComposer: Personalized Group Photo Generation via a Multi-Reference Canvas
Gordon Guocheng Qian, Ruihang Zhang, Tsai-Shien Chen +11
Existing personalized image generators still struggle to preserve multiple reference identities in natural and coherent multi-human generations. To address these limitations, we pr…
T2Bs: Text-to-Character Blendshapes via Video Generation
Jiahao Luo, Chaoyang Wang, Michael Vasilkovsky +8
We present T2Bs, a framework for generating high-quality, animatable character head morphable models from text by combining static text-to-3D generation with video diffusion. Text-…
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…
Can Text-to-Video Generation help Video-Language Alignment?
Luca Zanella, Massimiliano Mancini, Willi Menapace +3
Recent video-language alignment models are trained on sets of videos, each with an associated positive caption and a negative caption generated by large language models. A problem…
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…
I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning in Diffusion Models
Zhenxing Mi, Kuan-Chieh Wang, Guocheng Qian +5
This paper presents ThinkDiff, a novel alignment paradigm that empowers text-to-image diffusion models with multimodal in-context understanding and reasoning capabilities by integr…
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…
Towards Physical Understanding in Video Generation: A 3D Point Regularization Approach
Yunuo Chen, Junli Cao, Vidit Goel +5
We present a novel video generation framework that integrates 3-dimensional geometry and dynamic awareness. To achieve this, we augment 2D videos with 3D point trajectories and ali…
Nested Attention: Semantic-aware Attention Values for Concept Personalization
Or Patashnik, Rinon Gal, Daniil Ostashev +3
Personalizing text-to-image models to generate images of specific subjects across diverse scenes and styles is a rapidly advancing field. Current approaches often face challenges i…