title: Sci-Fi: Symmetric Constraint for Frame Inbetweening
summary: Frame inbetweening aims to synthesize intermediate video sequences
  conditioned on the given start and end frames. Current state-of-the-art methods
  mainly extend large-scale pre-trained Image-to-Video Diffusion models (I2V-DMs)
  by incorporating end-frame constraints via directly fine-tuning or omitting
  training. We identify a critical limitation in their design: Their injections
  of the end-frame constraint usually utilize the same mechanism that originally
  imposed the start-frame (single image) constraint. However, since the original
  I2V-DMs are adequately trained for the start-frame condition in advance,
  naively introducing the end-frame constraint by the same mechanism with much
  less (even zero) specialized training probably can't make the end frame have a
  strong enough impact on the intermediate content like the start frame. This
  asymmetric control strength of the two frames over the intermediate content
  likely leads to inconsistent motion or appearance collapse in generated frames.
  To efficiently achieve symmetric constraints of start and end frames, we
  propose a novel framework, termed Sci-Fi, which applies a stronger injection
  for the constraint of a smaller training scale. Specifically, it deals with the
  start-frame constraint as before, while introducing the end-frame constraint by
  an improved mechanism. The new mechanism is based on a well-designed
  lightweight module, named EF-Net, which encodes only the end frame and expands
  it into temporally adaptive frame-wise features injected into the I2V-DM. This
  makes the end-frame constraint as strong as the start-frame constraint,
  enabling our Sci-Fi to produce more harmonious transitions in various
  scenarios. Extensive experiments prove the superiority of our Sci-Fi compared
  with other baselines.
paper_authors._id: 68367078bec6153cee9be84c
paper_authors.name: Liuhan Chen
paper_authors._id: 68367078bec6153cee9be84d
paper_authors.name: Xiaodong Cun
paper_authors._id: 68367078bec6153cee9be84e
paper_authors.name: Xiaoyu Li
paper_authors._id: 68367078bec6153cee9be84f
paper_authors.name: Xianyi He
paper_authors._id: 68367078bec6153cee9be850
paper_authors.name: Shenghai Yuan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:58:01.230Z
paper_authors._id: 63468720dd6d90d82ccf3450
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63468720dd6d90d82ccf3450/tVBFlmZNz8FRMkOrDaDID.jpeg
paper_authors.fullname: YSH
paper_authors.type: user
paper_authors.user: BestWishYsh
paper_authors._id: 68367078bec6153cee9be851
paper_authors.name: Jie Chen
paper_authors._id: 68367078bec6153cee9be852
paper_authors.name: Ying Shan
paper_authors._id: 68367078bec6153cee9be853
paper_authors.name: Li Yuan
paper_title: Sci-Fi: Symmetric Constraint for Frame Inbetweening
paper_summary: Frame inbetweening aims to synthesize intermediate video sequences
  conditioned on the given start and end frames. Current state-of-the-art methods
  mainly extend large-scale pre-trained Image-to-Video Diffusion models (I2V-DMs)
  by incorporating end-frame constraints via directly fine-tuning or omitting
  training. We identify a critical limitation in their design: Their injections
  of the end-frame constraint usually utilize the same mechanism that originally
  imposed the start-frame (single image) constraint. However, since the original
  I2V-DMs are adequately trained for the start-frame condition in advance,
  naively introducing the end-frame constraint by the same mechanism with much
  less (even zero) specialized training probably can't make the end frame have a
  strong enough impact on the intermediate content like the start frame. This
  asymmetric control strength of the two frames over the intermediate content
  likely leads to inconsistent motion or appearance collapse in generated frames.
  To efficiently achieve symmetric constraints of start and end frames, we
  propose a novel framework, termed Sci-Fi, which applies a stronger injection
  for the constraint of a smaller training scale. Specifically, it deals with the
  start-frame constraint as before, while introducing the end-frame constraint by
  an improved mechanism. The new mechanism is based on a well-designed
  lightweight module, named EF-Net, which encodes only the end frame and expands
  it into temporally adaptive frame-wise features injected into the I2V-DM. This
  makes the end-frame constraint as strong as the start-frame constraint,
  enabling our Sci-Fi to produce more harmonious transitions in various
  scenarios. Extensive experiments prove the superiority of our Sci-Fi compared
  with other baselines.
paper_ai_summary: A novel framework named Sci-Fi addresses the inconsistency in frame control strength by introducing a stronger end-frame constraint mechanism, improving harmonious transitions in frame inbetweening.
paper_ai_keywords: frame inbetweening Image-to-Video Diffusion models I2V-DMs end-frame constraints start-frame constraint asymmetric control strength consistent motion appearance collapse EF-Net temporally adaptive frame-wise features