3 citations · 8 across the 14 of their papers we have counts for
10 papers · 1 filter
SkyReels-V3 Technique Report
Debang Li, Zhengcong Fei, Tuanhui Li +19
Video generation serves as a cornerstone for building world models, where multimodal contextual inference stands as the defining test of capability. In this end, we present SkyReel…
SkyReels-V2: Infinite-length Film Generative Model
Guibin Chen, Dixuan Lin, Jiangping Yang +22
Recent advances in video generation have been driven by diffusion models and autoregressive frameworks, yet critical challenges persist in harmonizing prompt adherence, visual qual…
SkyReels-A2: Compose Anything in Video Diffusion Transformers
Zhengcong Fei, Debang Li, Di Qiu +8
This paper presents SkyReels-A2, a controllable video generation framework capable of assembling arbitrary visual elements (e.g., characters, objects, backgrounds) into synthesized…
SkyReels-A1: Expressive Portrait Animation in Video Diffusion Transformers
Di Qiu, Zhengcong Fei, Rui Wang +5
We present SkyReels-A1, a simple yet effective framework built upon video diffusion Transformer to facilitate portrait image animation. Existing methodologies still encounter issue…
Ingredients: Blending Custom Photos with Video Diffusion Transformers
Zhengcong Fei, Debang Li, Di Qiu +2
This paper presents a powerful framework to customize video creations by incorporating multiple specific identity (ID) photos, with video diffusion Transformers, referred to as Ing…
Video Diffusion Transformers are In-Context Learners
Zhengcong Fei, Di Qiu, Debang Li +2
This paper investigates a solution for enabling in-context capabilities of video diffusion transformers, with minimal tuning required for activation. Specifically, we propose a sim…
MovieCharacter: A Tuning-Free Framework for Controllable Character Video Synthesis
Di Qiu, Zheng Chen, Rui Wang +4
Recent advancements in character video synthesis still depend on extensive fine-tuning or complex 3D modeling processes, which can restrict accessibility and hinder real-time appli…
Scaling Diffusion Transformers to 16 Billion Parameters
Zhengcong Fei, Mingyuan Fan, Changqian Yu +2
In this paper, we present DiT-MoE, a sparse version of the diffusion Transformer, that is scalable and competitive with dense networks while exhibiting highly optimized inference.…
Scalable Diffusion Models with State Space Backbone
Zhengcong Fei, Mingyuan Fan, Changqian Yu +1
This paper presents a new exploration into a category of diffusion models built upon state space architecture. We endeavor to train diffusion models for image data, wherein the tra…
Tuning-Free Inversion-Enhanced Control for Consistent Image Editing
Xiaoyue Duan, Shuhao Cui, Guoliang Kang +4
Consistent editing of real images is a challenging task, as it requires performing non-rigid edits (e.g., changing postures) to the main objects in the input image without changing…