洞見 - Machine Learning - # Text-to-Motion Generation

Bidirectional Autoregressive Diffusion for Generating Coherent and Diverse Text-Guided Human Motions

Q: How can the proposed BAD framework be extended to other sequence modeling tasks beyond text-to-motion generation, such as language modeling or audio synthesis?

The Bidirectional Autoregressive Diffusion (BAD) framework, with its innovative approach to sequence modeling, can be effectively extended to various tasks beyond text-to-motion generation. For instance, in language modeling, BAD can leverage its bidirectional context capturing capabilities to enhance the understanding of complex linguistic structures. By applying the same permutation-based corruption technique, the model can be trained to predict masked words in a sentence while considering both preceding and subsequent words, thus improving the coherence and fluency of generated text. In audio synthesis, BAD can be adapted to model audio waveforms or spectrograms by treating audio samples as discrete tokens. The framework's ability to capture long-range dependencies through its hybrid attention mechanism can facilitate the generation of high-fidelity audio sequences that maintain temporal coherence. Additionally, the incorporation of audio-specific features, such as pitch and timbre, can further enhance the model's performance in generating diverse and realistic audio outputs. Overall, the flexibility of the BAD framework allows it to be tailored for various sequence modeling tasks by adjusting the input representation and training objectives, thereby broadening its applicability across different domains.

Q: What are the potential limitations of the permutation-based corruption technique, and how could it be further improved to enhance the model's ability to capture long-range dependencies in the data?

While the permutation-based corruption technique employed in the BAD framework offers significant advantages in preserving the natural sequence structure, it does have potential limitations. One key limitation is that the random ordering of tokens may not adequately capture long-range dependencies, especially in sequences where relationships between distant tokens are crucial for understanding context. This could lead to suboptimal performance in tasks requiring a deep understanding of the entire sequence. To enhance the model's ability to capture long-range dependencies, several improvements could be considered. One approach is to implement a hierarchical corruption strategy, where tokens are grouped into segments, and the model learns to predict masked tokens within these segments while also considering inter-segment relationships. This could help the model maintain a broader context while still benefiting from the permutation-based approach. Another improvement could involve integrating attention mechanisms that specifically focus on long-range dependencies, such as dilated convolutions or recurrent layers, alongside the existing transformer architecture. By allowing the model to explicitly attend to distant tokens, it can better capture the intricate relationships that span across the entire sequence, ultimately leading to improved performance in tasks that require a comprehensive understanding of long-range dependencies.

Q: Given the success of BAD in text-guided motion generation, how could the framework be adapted to incorporate additional modalities, such as visual information, to enable more comprehensive and multimodal motion synthesis?

The success of the BAD framework in text-guided motion generation opens up exciting possibilities for incorporating additional modalities, such as visual information, to create a more comprehensive and multimodal motion synthesis system. To achieve this, the framework can be adapted in several ways. Firstly, visual information can be integrated by utilizing pre-trained vision models, such as convolutional neural networks (CNNs) or vision transformers, to extract meaningful features from images or video frames. These visual features can then be combined with the textual embeddings generated by the Contrastive Language-Image Pretraining (CLIP) model used in BAD. By conditioning the motion generation process on both text and visual inputs, the model can produce more contextually relevant and visually coherent motion sequences. Secondly, the corruption process can be extended to include visual tokens alongside motion tokens. For instance, during training, both motion and visual tokens can be randomly masked, and the model can be tasked with reconstructing the original sequences based on the available context from both modalities. This dual-modality corruption technique would encourage the model to learn richer representations that account for the interplay between text, motion, and visual information. Lastly, the attention mechanism within the transformer architecture can be modified to allow cross-modal attention, enabling the model to attend to both motion and visual tokens simultaneously. This would facilitate a more integrated understanding of how visual cues influence motion generation, leading to more realistic and contextually appropriate motion outputs. By incorporating these adaptations, the BAD framework can evolve into a powerful multimodal synthesis tool, capable of generating complex motion sequences that are informed by both textual and visual contexts.

核心概念

The proposed Bidirectional Autoregressive Diffusion (BAD) framework unifies the strengths of autoregressive and mask-based generative models to effectively capture both sequential and bidirectional relationships in text-guided human motion generation.

摘要

The paper introduces the Bidirectional Autoregressive Diffusion (BAD) framework, a novel pre-training strategy for sequence modeling that combines the advantages of autoregressive and mask-based generative models.

The key components of the framework are:

Motion Tokenizer: A simple VQ-VAE is used to transform raw 3D motion sequences into discrete motion tokens.
Conditional Mask-Based Transformer: The transformer is trained to reconstruct the original motion tokens from a corrupted sequence, conditioned on a text prompt. The corruption process utilizes a novel permutation-based technique that preserves the natural sequence structure while enforcing causal dependencies.

The hybrid attention mask constructed during training allows the model to effectively capture both sequential and bidirectional relationships in the motion data. This is in contrast to autoregressive models, which struggle to model complex bidirectional patterns, and mask-based models, which often assume token independence during prediction.

Extensive experiments on the HumanML3D and KIT-ML datasets demonstrate that BAD outperforms state-of-the-art autoregressive and mask-based motion models in terms of motion quality, text-motion alignment, and diversity. Additionally, BAD achieves comparable or superior results to advanced methods that utilize more complex motion tokenizers, highlighting its efficiency and effectiveness.

The paper also showcases the versatility of BAD by applying it to various text-guided motion editing tasks, such as inpainting, outpainting, prefix prediction, and suffix completion, further validating the model's ability to generate coherent and natural human motions.

客製化摘要

使用 AI 重寫

產生引用格式

翻譯原文

翻譯成其他語言

產生心智圖

從原文內容

前往原文

arxiv.org

統計資料

The paper reports the following key statistics:

The HumanML3D dataset contains 14,616 motion sequences and 44,970 textual descriptions.
The KIT-ML dataset contains 3,911 motion sequences and 6,278 textual annotations.

引述

"Autoregressive models excel in modeling sequential dependencies by enforcing causal constraints, yet they struggle to capture complex bidirectional patterns due to their unidirectional nature."
"Mask-based generative models leverage bidirectional context, enabling richer dependency modeling. However, they often assume token independence during prediction, which undermines the modeling of sequential dependencies."

從以下內容提煉的關鍵洞見

BAD: Bidirectional Auto-regressive Diffusion for Text-to-Motion Generation

by S. Rohollah ... 於 arxiv.org 09-18-2024

https://arxiv.org/pdf/2409.10847.pdf

BAD: Bidirectional Auto-regressive Diffusion for Text-to-Motion Generation

深入探究

How can the proposed BAD framework be extended to other sequence modeling tasks beyond text-to-motion generation, such as language modeling or audio synthesis?

The Bidirectional Autoregressive Diffusion (BAD) framework, with its innovative approach to sequence modeling, can be effectively extended to various tasks beyond text-to-motion generation. For instance, in language modeling, BAD can leverage its bidirectional context capturing capabilities to enhance the understanding of complex linguistic structures. By applying the same permutation-based corruption technique, the model can be trained to predict masked words in a sentence while considering both preceding and subsequent words, thus improving the coherence and fluency of generated text.
In audio synthesis, BAD can be adapted to model audio waveforms or spectrograms by treating audio samples as discrete tokens. The framework's ability to capture long-range dependencies through its hybrid attention mechanism can facilitate the generation of high-fidelity audio sequences that maintain temporal coherence. Additionally, the incorporation of audio-specific features, such as pitch and timbre, can further enhance the model's performance in generating diverse and realistic audio outputs.
Overall, the flexibility of the BAD framework allows it to be tailored for various sequence modeling tasks by adjusting the input representation and training objectives, thereby broadening its applicability across different domains.

What are the potential limitations of the permutation-based corruption technique, and how could it be further improved to enhance the model's ability to capture long-range dependencies in the data?

While the permutation-based corruption technique employed in the BAD framework offers significant advantages in preserving the natural sequence structure, it does have potential limitations. One key limitation is that the random ordering of tokens may not adequately capture long-range dependencies, especially in sequences where relationships between distant tokens are crucial for understanding context. This could lead to suboptimal performance in tasks requiring a deep understanding of the entire sequence.
To enhance the model's ability to capture long-range dependencies, several improvements could be considered. One approach is to implement a hierarchical corruption strategy, where tokens are grouped into segments, and the model learns to predict masked tokens within these segments while also considering inter-segment relationships. This could help the model maintain a broader context while still benefiting from the permutation-based approach.
Another improvement could involve integrating attention mechanisms that specifically focus on long-range dependencies, such as dilated convolutions or recurrent layers, alongside the existing transformer architecture. By allowing the model to explicitly attend to distant tokens, it can better capture the intricate relationships that span across the entire sequence, ultimately leading to improved performance in tasks that require a comprehensive understanding of long-range dependencies.

Given the success of BAD in text-guided motion generation, how could the framework be adapted to incorporate additional modalities, such as visual information, to enable more comprehensive and multimodal motion synthesis?

The success of the BAD framework in text-guided motion generation opens up exciting possibilities for incorporating additional modalities, such as visual information, to create a more comprehensive and multimodal motion synthesis system. To achieve this, the framework can be adapted in several ways.
Firstly, visual information can be integrated by utilizing pre-trained vision models, such as convolutional neural networks (CNNs) or vision transformers, to extract meaningful features from images or video frames. These visual features can then be combined with the textual embeddings generated by the Contrastive Language-Image Pretraining (CLIP) model used in BAD. By conditioning the motion generation process on both text and visual inputs, the model can produce more contextually relevant and visually coherent motion sequences.
Secondly, the corruption process can be extended to include visual tokens alongside motion tokens. For instance, during training, both motion and visual tokens can be randomly masked, and the model can be tasked with reconstructing the original sequences based on the available context from both modalities. This dual-modality corruption technique would encourage the model to learn richer representations that account for the interplay between text, motion, and visual information.
Lastly, the attention mechanism within the transformer architecture can be modified to allow cross-modal attention, enabling the model to attend to both motion and visual tokens simultaneously. This would facilitate a more integrated understanding of how visual cues influence motion generation, leading to more realistic and contextually appropriate motion outputs.
By incorporating these adaptations, the BAD framework can evolve into a powerful multimodal synthesis tool, capable of generating complex motion sequences that are informed by both textual and visual contexts.