Custom Motion Video Generation Without Frame-by-Frame Editing
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Solution Overview
Problem
Conventional video generation models struggle to efficiently transfer custom motions to new objects or actors, requiring costly and time-consuming frame-by-frame editing, and lack the ability to learn and reproduce generalized motion concepts across various subjects.
Innovation Solution
A video generation model that learns both custom motion and appearance concepts without separate adapter models, enabling the generation of synthetic videos with custom motions applied to any actor or object, using a diffusion model architecture and transformer-based text encoding to process text prompts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video generation models are used to transfer custom motions to new objects, then the motion can be transferred, but the process requires costly and time-consuming frame-by-frame editing
Solution Approach 1:
The patent extracts the motion concept from training videos and represents it as a separate motion embedding vector. This motion embedding is then injected into the text-to-video model independently from the visual content, allowing motion transfer without frame-by-frame editing. The motion concept is separated from the object-specific visual features, enabling efficient transfer to new objects.
Solution Approach 2:
The patent introduces motion embeddings as an intermediary representation that bridges the gap between training data and generated videos. These embeddings serve as a mediator that carries motion information from source videos to the text-to-video generation process, eliminating the need for direct frame-by-frame manipulation.
2Adaptability or versatility
If conventional models are used, then motion transfer is possible, but separate adapter models are required which increases system complexity
Solution Approach 1:
The patent merges the motion transfer capability directly into the text-to-video model by injecting motion embeddings into the existing generation pipeline. This integration eliminates the need for separate adapter models while maintaining motion transfer functionality. The motion embeddings are combined with text and image embeddings within the unified model architecture.
Solution Approach 2:
The patent creates a universal motion embedding representation that can be applied across different objects and contexts without requiring object-specific adapters. The same motion embedding mechanism works for transferring motions to various types of subjects, making the system multi-functional and reducing overall complexity.
3Adaptability or versatility
If custom motions are learned from training sets, then generalized motion concepts can be reproduced, but training data processing requires significant computational resources
Solution Approach 1:
The patent extracts only the essential motion information from training videos and encodes it into compact motion embeddings. This extraction process separates motion dynamics from visual content, creating a condensed representation that requires less computational energy to process while still capturing generalized motion concepts.
Solution Approach 2:
The patent transforms complex video data into a different parameter space through motion embeddings. By representing motions as vectors in an embedding space rather than as raw video frames, the system reduces computational requirements for training and inference while maintaining the ability to reproduce generalized motion concepts.
Data Source
AI summary
Systems and methods include generating synthetic videos based on a custom motion. A video generation system obtains a text prompt including an object and a custom motion token. The custom motion token represents a custom motion. The system encodes the text prompt to obtain a text embedding. Subsequently, a video generation model generates a synthetic video depicting the object performing the custom motion based on the text embedding using a video generation model.