Scene-Aware Motion Diffusion for Realistic 3D Human Interaction
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Solution Overview
Problem
Conventional human motion generation techniques struggle to generate realistic and generalizable human motion in 3D scenes due to the lack of suitable training data, leading to unrealistic and low-quality animations, and reinforcement learning methods are limited to specific interactions, failing to capture the full range of human motion subtleties.
Innovation Solution
A scene-aware motion diffusion model is developed by pre-training on motion data and incorporating a scene-aware component to extract and inject scene information, allowing for fine-tuning on limited motion-scene data to generate more accurate human motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional motion generation techniques use high-quality training data pairing captured human motion with corresponding 3D scenes, then motion realism is improved, but data collection complexity and cost increase significantly
Solution Approach 1:
The patent segments the training process into two independent stages: pre-training on large-scale motion data without scene information, and fine-tuning on limited motion-scene paired data. This segmentation allows the model to learn general motion patterns from abundant data sources while requiring minimal scene-aware training data, thus resolving the contradiction between motion realism and data collection complexity
Solution Approach 2:
The patent applies preliminary action by pre-training the diffusion model on extensive motion datasets before fine-tuning on scene-aware data. This preliminary training equips the model with fundamental motion generation capabilities using readily available data, while the subsequent fine-tuning on limited scene-aware data refines the model's understanding of spatial relationships and object interactions, thereby achieving high motion realism without requiring comprehensive scene-aware training data from the outset
2Reliability
If conventional techniques place high-quality motion capture sequences into scanned scene environments, then scene navigation is improved, but human behavior realism deteriorates
Solution Approach 1:
The patent merges scene awareness capabilities with motion generation by integrating scene representation inputs directly into the diffusion model architecture. This merging allows the model to simultaneously learn from motion data and adapt to scene contexts, generating motions that are both physically plausible and behaviorally realistic for the specific scene environment, thus resolving the contradiction between scene navigation reliability and human behavior realism
Solution Approach 2:
The patent changes the model's parameter configuration by adding scene representation inputs (such as 3D scene graphs, object relationships, and spatial configurations) as conditioning parameters during the diffusion process. This enables the model to adjust motion generation parameters dynamically based on scene context, producing behaviors that are adapted to specific environmental conditions rather than using generic motion sequences, thereby improving human behavior realism while maintaining scene navigation capability
3Quantity of substance
If reinforcement learning is used to address lack of training data by training separate policies for each interaction type, then data requirements are reduced, but motion variety and subtleties are limited
Solution Approach 1:
The patent applies universality by training a single diffusion model that can generate diverse motion types through a unified architecture conditioned on scene representations and text prompts. This universal model learns to handle multiple interaction scenarios (sitting, standing, walking, object manipulation) within one framework, eliminating the need for separate policies for each interaction type while maintaining the ability to generate varied and subtle motions across different contexts, thus resolving the contradiction between reduced data requirements and maintained motion variety
Data Source
AI summary
A motion diffusion model may be pre-trained on motion data, and a scene-aware component (e.g., one or more layers of a neural network) may be connected and used to extract and inject a representation of scene information into the pre-trained motion diffusion model. For example, to predict orientations of joint waypoints along a path through a particular 3D scene, a scene-aware input channel that accepts a representation of the 3D structure of the scene may be added to a pre-trained motion diffusion model. To predict orientations of joint waypoints along a path that interacts with a 3D object in the 3D scene, a scene-aware input channel that accepts a representation of the 3D object and/or a surface thereof may be added to a pre-trained motion diffusion model. As such, the resulting scene-aware motion diffusion model(s) may be tuned on motion-scene data and used to generate human motion.