Timeline-Controlled Human Motion Simulation From Complex Text Prompts
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Solution Overview
Problem
Conventional generative human motion simulation techniques struggle to synthesize realistic and precise animations from complex text prompts that specify sequential and simultaneous actions, often resulting in unrealistic and inaccurate animations due to a lack of representative training data and imprecise user input.
Innovation Solution
A timeline-based approach is employed to iteratively denoise motion sequences using a pre-trained diffusion model, allowing for the independent denoising of motion segments corresponding to each text prompt, followed by spatial and temporal stitching to generate compositional human motion that accurately reflects the specified actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional text-to-motion generation techniques are used to synthesize human motion from complex text prompts, then the system can handle simple single-action prompts, but it fails to accurately generate motion for complex prompts specifying sequential and simultaneous actions
Solution Approach 1:
The patent divides a complex text prompt into multiple temporal segments, each corresponding to a specific action or phase of motion. The timeline is split into discrete intervals, with each interval associated with a simplified text prompt that describes a single action. This segmentation allows the system to process complex prompts by breaking them down into manageable, accurate motion generation tasks for each time segment.
2Measurement precision
If detailed text prompts are used to specify precise timing and duration of actions, then the user intent can be captured, but the prompts become unwieldy and ambiguous
Solution Approach 1:
The patent segments the detailed timing and duration specifications into discrete temporal intervals on a timeline. Each interval is associated with a simple action descriptor rather than a lengthy detailed prompt. This segmentation maintains measurement precision by defining clear start and end times for each action while improving ease of operation by using concise action descriptors for each segment.
Solution Approach 2:
The patent employs a dynamic timeline structure where temporal intervals and action assignments can be flexibly adjusted. The system allows for dynamic modification of the timeline segments, enabling users to easily modify timing and duration by adjusting interval boundaries rather than rewriting complex prompts. This dynamic structure maintains precision while improving operational simplicity.
3Device complexity
If a single text prompt is used to generate fixed duration motion, then the generation process is simple, but the system lacks control over action timing and sequencing
Solution Approach 1:
The patent segments the motion generation process into multiple temporal intervals, each with its own simplified text prompt and motion generation task. This segmentation maintains relative simplicity by processing each segment independently while providing precise temporal control through the timeline structure. Each segment can be generated with simple prompts, yet the overall system achieves complex temporal control through the organized sequence of segments.
Solution Approach 2:
The patent introduces a temporal dimension by organizing motion generation along a timeline axis. Instead of generating motion in a single fixed duration from one prompt, the system distributes motion generation across multiple time points, adding temporal control as a new dimension. This allows independent control of timing and duration for each action while maintaining simplicity in the generation process for each segment.
4Productivity
If conventional techniques generate motion without temporal segmentation, then the process is straightforward, but the generated motion does not reflect realistic human behavior for complex actions
Solution Approach 1:
The patent segments the motion generation process into temporal intervals that correspond to realistic phases of human action. Each segment is generated with appropriate timing and transitions, reflecting how humans actually perform complex actions in sequences. This segmentation maintains efficiency by processing segments independently while improving realism by aligning generated motion with natural human behavior patterns for each temporal phase.
Data Source
AI summary
In various examples, a timeline of text prompt(s) specifying any number of (e.g., sequential and/or simultaneous) actions may be specified or generated, and the timeline may be used to drive a diffusion model to generate compositional human motion that implements the arrangement of action(s) specified by the timeline. For example, at each denoising step, a pre-trained motion diffusion model may be used to denoise a motion segment corresponding to each text prompt independently of the others, and the resulting denoised motion segments may be temporally stitched, and/or spatially stitched based on body part labels associated with each text prompt. As such, the techniques described herein may be used to synthesize realistic motion that accurately reflects the semantics and timing of the text prompt(s) specified in the timeline.