title: Frame In-N-Out: Unbounded Controllable Image-to-Video Generation
summary: Controllability, temporal coherence, and detail synthesis remain the most
  critical challenges in video generation. In this paper, we focus on a commonly
  used yet underexplored cinematic technique known as Frame In and Frame Out.
  Specifically, starting from image-to-video generation, users can control the
  objects in the image to naturally leave the scene or provide breaking new
  identity references to enter the scene, guided by user-specified motion
  trajectory. To support this task, we introduce a new dataset curated
  semi-automatically, a comprehensive evaluation protocol targeting this setting,
  and an efficient identity-preserving motion-controllable video Diffusion
  Transformer architecture. Our evaluation shows that our proposed approach
  significantly outperforms existing baselines.
paper_authors._id: 683672b0bf8d50a1f8ae8741
paper_authors.name: Boyang Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:57:46.426Z
paper_authors._id: 64ed876a74d9b58eabc769a4
paper_authors.avatarUrl: /avatars/9cf8af8e6f428b75827458b63d376ee3.svg
paper_authors.fullname: Boyang Wang
paper_authors.type: user
paper_authors.user: HikariDawn
paper_authors._id: 683672b0bf8d50a1f8ae8742
paper_authors.name: Xuweiyi Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:39:05.553Z
paper_authors._id: 634632aaac1cb29fb2ac9f14
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/634632aaac1cb29fb2ac9f14/nGZ2TzKOOcKMAR_NFYKkR.jpeg
paper_authors.fullname: Xuweiyi Chen
paper_authors.type: user
paper_authors.user: Xuweiyi
paper_authors._id: 683672b0bf8d50a1f8ae8743
paper_authors.name: Matheus Gadelha
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:39:11.640Z
paper_authors._id: 6315a9c466062476ae1fa0c7
paper_authors.avatarUrl: /avatars/c59749be7ecee4858c59889fea61c88a.svg
paper_authors.fullname: Matheus
paper_authors.type: user
paper_authors.user: matheusgadelha
paper_authors._id: 683672b0bf8d50a1f8ae8744
paper_authors.name: Zezhou Cheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:39:22.963Z
paper_authors._id: 64834da6c8b6f4a798f9f09d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64834da6c8b6f4a798f9f09d/EOxUisexBKD-L5yCD7Ecs.png
paper_authors.fullname: Cheng
paper_authors.type: user
paper_authors.user: Zezhou
paper_title: Frame In-N-Out: Unbounded Controllable Image-to-Video Generation
paper_summary: Controllability, temporal coherence, and detail synthesis remain the most
  critical challenges in video generation. In this paper, we focus on a commonly
  used yet underexplored cinematic technique known as Frame In and Frame Out.
  Specifically, starting from image-to-video generation, users can control the
  objects in the image to naturally leave the scene or provide breaking new
  identity references to enter the scene, guided by user-specified motion
  trajectory. To support this task, we introduce a new dataset curated
  semi-automatically, a comprehensive evaluation protocol targeting this setting,
  and an efficient identity-preserving motion-controllable video Diffusion
  Transformer architecture. Our evaluation shows that our proposed approach
  significantly outperforms existing baselines.
paper_ai_summary: An efficient Diffusion Transformer architecture addresses controllability, temporal coherence, and detail synthesis in video generation using the Frame In and Frame Out technique.
paper_ai_keywords: image-to-video generation motion trajectory video Diffusion Transformer