ShotAdapter – A multi – lens video generation framework jointly launched by Adobe and UIUC

What is ShotAdapter?

ShotAdapter is a framework for text-to-multi-shot video generation, jointly developed by Adobe and the University of Illinois Urbana-Champaign (UIUC). It is based on fine-tuning a pretrained text-to-video model, introducing transition tokens and a localized attention masking strategy to enable the generation of coherent multi-shot videos.

ShotAdapter ensures character identity consistency across different shots and allows users to control the number, duration, and content of shots via specific textual prompts. It also introduces a novel method for constructing multi-shot video datasets from single-shot video datasets by sampling, segmenting, and stitching together video clips for training purposes.

Key Features of ShotAdapter

• Multi-Shot Video Generation: Generates videos composed of multiple shots based on textual descriptions, with varying actions and backgrounds across shots.

• Control Over Shot Count and Duration: Users can specify the number and duration of shots in the video through textual prompts.

• Character Identity Consistency: Maintains consistent character identities throughout multiple shots.

• Background Control: Allows users to either preserve the background across shots or switch to new backgrounds between shots as needed.

• Shot-Specific Content Control: Enables fine-grained control over the content of each individual shot based on shot-specific textual prompts.

Technical Foundations of ShotAdapter

• Transition Tokens: Introduces special tokens to indicate shot transitions within a video. These tokens are embedded into the text-to-video model, enabling it to detect and generate smooth transitions between shots.

• Localized Attention Masking: Applies a masking strategy that restricts cross-shot interactions in the model’s attention mechanism. This ensures each textual prompt affects only the corresponding video frames, enabling precise control over individual shots.

• Fine-Tuning a Pretrained Model: Fine-tunes a pretrained text-to-video model on a multi-shot video dataset. The fine-tuning requires relatively few iterations (e.g., around 5,000) for the model to adapt to the multi-shot generation task.

• Dataset Construction: Proposes a method for constructing multi-shot video datasets from existing single-shot datasets. This involves sampling, segmenting, and stitching video clips, along with post-processing steps such as identity consistency checks and generating shot-specific captions.

Project Links

• Project Website: https://shotadapter.github.io/

• arXiv Paper: https://arxiv.org/pdf/2505.07652

Application Scenarios for ShotAdapter

• Film and TV Production: Generate previews, animations, and visual effects from scripts to improve production efficiency.

• Advertising and Marketing: Create engaging advertisements and social media content to boost user engagement.

• Education: Assist in teaching and training by producing instructional and corporate training videos.

• Game Development: Generate in-game cutscenes and cinematic sequences to enhance player experience.

• Personal Creativity: Empower individuals to create video diaries and creative content, inspiring personal storytelling and artistic expression.