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Found 11 papers, 10 papers with code
SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers
The generation and editing of audio-conditioned talking portraits guided by multimodal inputs, including text, images, and videos, remains under explored.
SkyReels-V2: Infinite-length Film Generative Model
Recent advances in video generation have been driven by diffusion models and autoregressive frameworks, yet critical challenges persist in harmonizing prompt adherence, visual quality, motion dynamics, and duration: compromises in motion dynamics to enhance temporal visual quality, constrained video duration (5-10 seconds) to prioritize resolution, and inadequate shot-aware generation stemming from general-purpose MLLMs' inability to interpret cinematic grammar, such as shot composition, actor expressions, and camera motions.
SkyReels-A2: Compose Anything in Video Diffusion Transformers
This paper presents SkyReels-A2, a controllable video generation framework capable of assembling arbitrary visual elements (e. g., characters, objects, backgrounds) into synthesized videos based on textual prompts while maintaining strict consistency with reference images for each element.
Ingredients: Blending Custom Photos with Video Diffusion Transformers
This paper presents a powerful framework to customize video creations by incorporating multiple specific identity (ID) photos, with video diffusion Transformers, referred to as \texttt{Ingredients}.
Video Diffusion Transformers are In-Context Learners
This paper investigates a solution for enabling in-context capabilities of video diffusion transformers, with minimal tuning required for activation.
Scaling Diffusion Transformers to 16 Billion Parameters
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.
Dimba: Transformer-Mamba Diffusion Models
This paper unveils Dimba, a new text-to-image diffusion model that employs a distinctive hybrid architecture combining Transformer and Mamba elements.
Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models
Transformers have catalyzed advancements in computer vision and natural language processing (NLP) fields.
Composing Good Shots by Exploiting Mutual Relations
However, the mutual relations between the candidates from an image play an essential role in composing a good shot due to the comparative nature of this problem.
Learning to Learn Cropping Models for Different Aspect Ratio Requirements
In addition, both the intermediate and final results show that the proposed model can predict different cropping windows for an image depending on different aspect ratio requirements.
A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping
Image cropping aims at improving the aesthetic quality of images by adjusting their composition.
