ShotAdapter - A multi-lens video generation framework jointly developed by Adobe and UIUC

What is a ShotAdapter?

ShotAdapter, a framework developed by Adobe in collaboration with UIUC, is used for text-to-multi-shot video generation. Based on a fine-tuned pre-trained text-to-video model, it introduces transition markers and local attention masking strategies to generate multi-shot videos. The framework ensures consistency of character identities across different shots and allows users to control the number, duration, and content of shots using specific text cues. ShotAdapter introduces a new method for constructing multi-shot video datasets from single-shot video datasets, generating training data based on sampling, segmenting, and stitching video clips.

Main functions of ShotAdapter

• Multi-camera video generationGenerate a video containing multiple shots based on a text description, each shot having different activities and backgrounds.
• Number of shots and duration controlUsers can precisely control the number of shots and the duration of each shot in a video using text prompts.
• Consistency of Role IdentityMaintain the character's identity consistently across multiple shots.
• Background controlIt supports maintaining a consistent background throughout the video or switching to a new background between shots, allowing for flexible adjustments based on user needs.
• Lens-specific content controlIt supports users in controlling the content of each shot based on specific text prompts, enabling fine-grained control over video details.

Technical Principles of ShotAdapter

• Transition markersSpecial transition markers are introduced to indicate shot transitions in the video. These markers are embedded in the text-to-video model, allowing the model to recognize and generate transitions between shots.
• Local attention maskTo ensure a close correspondence between the content of each shot and the text cues, local attention masking is used. This masking strategy restricts the interaction between different parts of the model, ensuring that each text cue only affects its corresponding video frame, thus achieving shot-specific control.
• Fine-tuning the pre-trained modelThis method generates multi-camera videos by fine-tuning a pre-trained text-to-video model on a multi-camera video dataset. The fine-tuning process requires only a relatively small number of iterations (e.g., 5000) to adapt the model to the multi-camera video generation task.
• Dataset ConstructionTo train a multi-shot video generation model, this paper presents a method for constructing a multi-shot video dataset from a single-shot video dataset. Based on sampling, segmentation, and stitching of video segments, and post-processing steps (such as identity consistency checks and shot-specific caption generation), a suitable multi-shot video dataset for training is created.

ShotAdapter project address

• Project official website:https://shotadapter.github.io/
• arXiv technical paper:https://arxiv.org/pdf/2505.07652

Application scenarios of ShotAdapter

• Film and television productionGenerate script previews, animations, and special effects videos to improve production efficiency.
• Advertising and MarketingCreate engaging ads and social media videos to increase user engagement.
• Education: To assist in teaching and training, and to produce teaching videos and corporate training content.
• Game developmentGenerate game story videos and cutscenes to enhance the player experience.
• Personal creationIt helps individuals create video diaries and creative videos, inspiring their creativity.