Frame In-N-Out: Unbounded Controllable Image-to-Video Generation
Authors: name: Boyang Wang, name: Xuweiyi Chen, name: Matheus Gadelha, name: Zezhou Cheng

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.