Text-to-Motion Latent Sequence Generation for Smooth Long-Term Motion
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Solution Overview
Problem
Existing methods for generating long-term human motion from text inputs face challenges such as the scarcity of long-term training data, reliance on sequential datasets, and the generation of unrealistic gaps between motions due to discontinuities in the generation process.
Innovation Solution
A method using a 1D convolutional VQVAE autoencoder that compresses motion into discrete latent vectors, allowing for continuous decoding of long-term motion without sequential data, by training a text encoder to predict latent sequences and a motion decoder to reconstruct smooth transitions based on text and duration inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods use sequential datasets for training long-term motion generation, then they can generate motion sequences, but the generation process produces unrealistic gaps and discontinuities between motions
Solution Approach 1:
The patent segments the motion generation task into two independent parts: a VQVAE model that processes individual motion clips and a text encoder that generates latent sequences from text descriptions. This segmentation allows each component to specialize in its function, with the VQVAE ensuring smooth intra-clip motion and the text encoder providing coherent inter-clip transitions, thereby eliminating the gaps and discontinuities present in sequential generation methods
Solution Approach 2:
The patent introduces a text encoder as an intermediary component that bridges the gap between text inputs and motion generation. The text encoder converts text descriptions into latent sequences that serve as intermediaries for guiding the VQVAE motion generation process, enabling smooth transitions between different motion clips without the discontinuities inherent in direct sequential generation
2Adaptability or versatility
If existing methods rely on sequential training data, then they can capture motion patterns, but long-term training data is scarce and difficult to obtain
Solution Approach 1:
The patent divides the training data requirement into two separate datasets: a motion dataset for training the VQVAE model and a text-description dataset for training the text encoder. This segmentation allows each model to be trained on appropriately labeled data, eliminating the need for scarce long-term sequential data while still capturing comprehensive motion patterns and text-motion relationships
Solution Approach 2:
The VQVAE model serves multiple functions: it encodes motion clips into latent representations, decodes them back to motion sequences, and provides a shared latent space that the text encoder utilizes for text-to-motion generation. This multi-functionality reduces the need for separate specialized datasets for each function, making the system more adaptable to various motion generation tasks
3Productivity
If existing methods use discontinuous generation processes, then they can generate motion sequences step-by-step, but the transitions between steps are unrealistic
Solution Approach 1:
The patent ensures continuity of useful action by generating motion clips that are semantically and temporally coherent throughout the entire sequence. The text encoder produces continuous latent sequences that guide the VQVAE to generate smooth transitions between clips, eliminating the discontinuities and gaps present in step-by-step generation methods while maintaining generation efficiency
Data Source
AI summary
A method for training a model for generating a representation of long-term motion from a text input comprises: training a motion encoder of an autoencoder to compress and map an input motion into a latent representation comprising a sequence of latent vectors in a discrete latent space, each latent vector representing a fixed length of motion; training a quantization module to quantize the latent vectors to a sequence of quantized latent vectors in quantized latent space; and training a motion decoder to reconstruct the quantized sequence as a sequence of single-frame pose representations. A text encoder is trained to predict a latent sequence conditioned on a text input and a duration using the mapped latent representation as a target.