title: TransPixar: Advancing Text-to-Video Generation with Transparency
summary: Text-to-video generative models have made significant strides, enabling
  diverse applications in entertainment, advertising, and education. However,
  generating RGBA video, which includes alpha channels for transparency, remains
  a challenge due to limited datasets and the difficulty of adapting existing
  models. Alpha channels are crucial for visual effects (VFX), allowing
  transparent elements like smoke and reflections to blend seamlessly into
  scenes. We introduce TransPixar, a method to extend pretrained video models for
  RGBA generation while retaining the original RGB capabilities. TransPixar
  leverages a diffusion transformer (DiT) architecture, incorporating
  alpha-specific tokens and using LoRA-based fine-tuning to jointly generate RGB
  and alpha channels with high consistency. By optimizing attention mechanisms,
  TransPixar preserves the strengths of the original RGB model and achieves
  strong alignment between RGB and alpha channels despite limited training data.
  Our approach effectively generates diverse and consistent RGBA videos,
  advancing the possibilities for VFX and interactive content creation.
paper_authors._id: 677c92224a867d8292b0a4d6
paper_authors.name: Luozhou Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-07T09:50:03.830Z
paper_authors._id: 6340333a733f9eef46913dc8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6340333a733f9eef46913dc8/BwhKgkqbuuxWsJv2s_JEH.png
paper_authors.fullname: luozhou wang
paper_authors.type: user
paper_authors.user: wileewang
paper_authors._id: 677c92224a867d8292b0a4d7
paper_authors.name: Yijun Li
paper_authors._id: 677c92224a867d8292b0a4d8
paper_authors.name: Zhifei Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-07T09:50:28.496Z
paper_authors._id: 634a91c53a0cd2d4985dc98a
paper_authors.avatarUrl: /avatars/fe680e683761c347cfa4bc9273b67b0d.svg
paper_authors.fullname: ZhiFei Chen
paper_authors.type: user
paper_authors.user: zhifeichen097
paper_authors._id: 677c92224a867d8292b0a4d9
paper_authors.name: Jui-Hsien Wang
paper_authors._id: 677c92224a867d8292b0a4da
paper_authors.name: Zhifei Zhang
paper_authors._id: 677c92224a867d8292b0a4db
paper_authors.name: He Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-07T09:50:58.619Z
paper_authors._id: 646c333136505117e22f08eb
paper_authors.avatarUrl: /avatars/6b720565b3916a6f7d1bb7a73cd99a33.svg
paper_authors.fullname: He Zhang
paper_authors.type: user
paper_authors.user: HeZhang
paper_authors._id: 677c92224a867d8292b0a4dc
paper_authors.name: Zhe Lin
paper_authors._id: 677c92224a867d8292b0a4dd
paper_authors.name: Yingcong Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-07T09:51:09.776Z
paper_authors._id: 655cba1d87b67834000590e8
paper_authors.avatarUrl: /avatars/3bd43b7c9351f65b8f38f4c8237a0146.svg
paper_authors.fullname: Yingcong Chen
paper_authors.type: user
paper_authors.user: yingcongchen
paper_title: TransPixar: Advancing Text-to-Video Generation with Transparency
paper_summary: Text-to-video generative models have made significant strides, enabling
  diverse applications in entertainment, advertising, and education. However,
  generating RGBA video, which includes alpha channels for transparency, remains
  a challenge due to limited datasets and the difficulty of adapting existing
  models. Alpha channels are crucial for visual effects (VFX), allowing
  transparent elements like smoke and reflections to blend seamlessly into
  scenes. We introduce TransPixar, a method to extend pretrained video models for
  RGBA generation while retaining the original RGB capabilities. TransPixar
  leverages a diffusion transformer (DiT) architecture, incorporating
  alpha-specific tokens and using LoRA-based fine-tuning to jointly generate RGB
  and alpha channels with high consistency. By optimizing attention mechanisms,
  TransPixar preserves the strengths of the original RGB model and achieves
  strong alignment between RGB and alpha channels despite limited training data.
  Our approach effectively generates diverse and consistent RGBA videos,
  advancing the possibilities for VFX and interactive content creation.
paper_ai_summary: TransPixar extends pretrained video models to generate RGBA videos using a diffusion transformer with LoRA-based fine-tuning, ensuring consistency between RGB and alpha channels.
paper_ai_keywords: diffusion transformer DiT alpha-specific tokens LoRA-based fine-tuning