Zero-Shot Image-to-Video Animation With Trajectory Control
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Solution Overview
Problem
Existing controllable video generation methods, such as text-to-image and image-to-image models, do not allow for fine-grained control of animation, particularly in terms of object movement trajectories, without requiring extensive training data and computational resources.
Innovation Solution
The proposed IMG2VIDANIM-ZERO (IVA0) model decomposes the controllable Image-to-Video (I2V) task into two atomic tasks: Out-of-place Motion Generation and In-place Motion Animation. It uses a pre-trained text-to-image model and incorporates gated self-attention layers for precise object placement and temporal attention layers for smooth motion transitions, all without the need for I2V-specific training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing controllable video generation methods are used, then basic animation can be achieved, but fine-grained control of object movement trajectories is not possible without extensive training data and computational resources
Solution Approach 1:
The model segments the video generation task into two independent components: structure generation (layout and composition) and motion generation (object trajectories and movements). This segmentation allows each component to be optimized separately, enabling fine-grained control of motion trajectories without requiring extensive annotated training data for the entire video generation process.
Solution Approach 2:
The patent introduces an intermediary structure generation model that creates static layouts which then guide the motion generation process. This intermediary step decouples the control of object positions from motion trajectories, allowing users to precisely control object movements along trajectories without needing to train a single end-to-end video generation model with extensive annotated data.
2Measurement precision
If existing controllable video generation methods are used, then basic animation can be achieved, but fine-grained control of object movement trajectories is not possible without extensive computational resources
Solution Approach 1:
By dividing the video generation into structure generation and motion generation stages, each stage can use computationally efficient models optimized for their specific task. The structure generation model processes static images efficiently, while the motion generation model only processes object trajectories, significantly reducing overall computational requirements compared to training a full video generation model.
Solution Approach 2:
The structure generation model performs preliminary action by creating the static layout and object positions before motion generation begins. This preliminary structure provides a fixed framework that guides subsequent motion generation, reducing the computational complexity of trajectory control since the spatial relationships are already established and do not require real-time computation.
3Reliability
If I2V-specific training data is collected and used, then training accuracy may improve, but data collection time and costs increase
Solution Approach 1:
The patent segments the training data requirements into separate components: the structure generation model can be trained on standard image datasets, while the motion generation model can be trained on simpler trajectory annotations. This segmentation eliminates the need for costly and time-consuming collection of fully annotated video data with precise object trajectories, maintaining training accuracy while dramatically reducing data collection time.
Solution Approach 2:
The structure generation model performs preliminary action by creating accurate object layouts and compositions before motion generation. This preliminary structural accuracy provides a strong foundation that reduces the amount of motion-specific training data needed, since the spatial framework is already well-established from training on standard image datasets rather than requiring full video annotation.
Data Source
AI summary
Techniques are generally described for controllable image-to-video generation. In various examples, a first image representing at least a first object may be received. First input data including a selection of the first object in the first image for animation may be received. Second input data including at least a first bounding box indicating a target location of the first object may be received. A latent diffusion text-to-image model and the first image may be used to generate a first plurality of visual tokens. One or more first grounding tokens may be generated representing a location of the first bounding box. The latent diffusion text-to-image model may be used to generate a video animating the first object based on the first plurality of visual tokens and the one or more first grounding tokens.