title: SkyReels-A2: Compose Anything in Video Diffusion Transformers
summary: 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.
paper_authors._id: 67ef3dfae8b932ae7a832950
paper_authors.name: Zhengcong Fei
paper_authors.status: admin_assigned
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paper_authors.fullname: zhengcong fei
paper_authors.type: user
paper_authors.user: onion
paper_authors._id: 67ef3dfae8b932ae7a832951
paper_authors.name: Debang Li
paper_authors.status: admin_assigned
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paper_authors.fullname: Li
paper_authors.type: user
paper_authors.user: Debang
paper_authors._id: 67ef3dfae8b932ae7a832952
paper_authors.name: Di Qiu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-04T07:19:41.458Z
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paper_authors.avatarUrl: /avatars/d40b0d7dda21fa1a68c291d11bc357ec.svg
paper_authors.fullname: Qiu Di
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paper_authors.user: diqiu7
paper_authors._id: 67ef3dfae8b932ae7a832953
paper_authors.name: Jiahua Wang
paper_authors._id: 67ef3dfae8b932ae7a832954
paper_authors.name: Yikun Dou
paper_authors._id: 67ef3dfae8b932ae7a832955
paper_authors.name: Rui Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-04T07:20:11.206Z
paper_authors._id: 62e0f1314db2175cd270ad08
paper_authors.avatarUrl: /avatars/1d3d6af6c63557f4abf0484e028fa942.svg
paper_authors.fullname: Rui Wang
paper_authors.type: user
paper_authors.user: ruiwang
paper_authors._id: 67ef3dfae8b932ae7a832956
paper_authors.name: Jingtao Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-04T07:20:20.880Z
paper_authors._id: 666a674967c686801acf25bb
paper_authors.avatarUrl: /avatars/c1f3edd63fd378dfb555e6413a966932.svg
paper_authors.fullname: jingtao xu
paper_authors.type: user
paper_authors.user: raul678
paper_authors._id: 67ef3dfae8b932ae7a832957
paper_authors.name: Mingyuan Fan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-04T07:20:32.597Z
paper_authors._id: 634672bfb7b4e71c7f45360f
paper_authors.avatarUrl: /avatars/4b646fc3e271be90b9ec619d42ce3e99.svg
paper_authors.fullname: Fan Mingyuan
paper_authors.type: user
paper_authors.user: MichaelFan
paper_authors._id: 67ef3dfae8b932ae7a832958
paper_authors.name: Guibin Chen
paper_authors._id: 67ef3dfae8b932ae7a832959
paper_authors.name: Yang Li
paper_authors._id: 67ef3dfae8b932ae7a83295a
paper_authors.name: Yahui Zhou
paper_title: SkyReels-A2: Compose Anything in Video Diffusion Transformers
paper_summary: 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.
paper_ai_summary: SkyReels-A2, an open-source framework, generates high-quality, element-controlled videos from textual prompts using a novel image-text embedding model, optimized inference pipeline, and A2 Bench for systematic evaluation.
paper_ai_keywords: elements-to-video (E2V) image-text joint embedding generative process prompt-reference-video triplets output stability A2 Bench