SkyReels-A2: Compose Anything in Video Diffusion Transformers
Authors: name: Zhengcong Fei, name: Debang Li, name: Di Qiu, name: Jiahua Wang, name: Yikun Dou, name: Rui Wang, name: Jingtao Xu, name: Mingyuan Fan, name: Guibin Chen, name: Yang Li, name: Yahui Zhou

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. We term this task
elements-to-video (E2V), whose primary challenges lie in preserving the
fidelity of each reference element, ensuring coherent composition of the scene,
and achieving natural outputs. To address these, we first design a
comprehensive data pipeline to construct prompt-reference-video triplets for
model training. Next, we propose a novel image-text joint embedding model to
inject multi-element representations into the generative process, balancing
element-specific consistency with global coherence and text alignment. We also
optimize the inference pipeline for both speed and output stability. Moreover,
we introduce a carefully curated benchmark for systematic evaluation, i.e, A2
Bench. Experiments demonstrate that our framework can generate diverse,
high-quality videos with precise element control. SkyReels-A2 is the first
open-source commercial grade model for the generation of E2V, performing
favorably against advanced closed-source commercial models. We anticipate
SkyReels-A2 will advance creative applications such as drama and virtual
e-commerce, pushing the boundaries of controllable video generation.