REVIEW 3 cited by
MotionBridge: Dynamic Video Inbetweening with Flexible Controls
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
By generating plausible and smooth transitions between two image frames, video inbetweening is an essential tool for video editing and long video synthesis. Traditional works lack the capability to generate complex large motions. While recent video generation techniques are powerful in creating high-quality results, they often lack fine control over the details of intermediate frames, which can lead to results that do not align with the creative mind. We introduce MotionBridge, a unified video inbetweening framework that allows flexible controls, including trajectory strokes, keyframes, masks, guide pixels, and text. However, learning such multi-modal controls in a unified framework is a challenging task. We thus design two generators to extract the control signal faithfully and encode feature through dual-branch embedders to resolve ambiguities. We further introduce a curriculum training strategy to smoothly learn various controls. Extensive qualitative and quantitative experiments have demonstrated that such multi-modal controls enable a more dynamic, customizable, and contextually accurate visual narrative.
Forward citations
Cited by 3 Pith papers
-
EF-VI: Enhancing End-Frame Injection for Video Inbetweening
EF-VI injects temporally expanded end-frame features into a transformer-based image-to-video diffusion model, improving video inbetweening quality over direct fine-tuning and bidirectional sampling baselines.
-
IKMo: Image-Keyframed Motion Generation with Trajectory-Pose Conditioned Motion Diffusion Model
A motion diffusion model with decoupled trajectory and keyframe-pose control, wrapped in an MLLM agent system, produces more controllable 3D human motion from images and text.
-
ATI: Any Trajectory Instruction for Controllable Video Generation
ATI injects user-drawn point trajectories as soft Gaussian feature masks into a pretrained image-to-video diffusion model, enabling unified camera, object, and local motion control.
Discussion (0). Continue with ORCID to comment.