BAMM: Bidirectional Autoregressive Motion Model

• URL: http://arxiv.org/abs/2403.19435v3
• Date: Mon, 1 Apr 2024 13:02:20 GMT
• Title: BAMM: Bidirectional Autoregressive Motion Model
• Authors: Ekkasit Pinyoanuntapong, Muhammad Usama Saleem, Pu Wang, Minwoo Lee, Srijan Das, Chen Chen,
• Abstract summary: Bidirectional Autoregressive Motion Model (BAMM) is a novel text-to-motion generation framework. BAMM consists of two key components: a motion tokenizer that transforms 3D human motion into discrete tokens in latent space, and a masked self-attention transformer that autoregressively predicts randomly masked tokens. This feature enables BAMM to simultaneously achieving high-quality motion generation with enhanced usability and built-in motion editability.
• Score: 14.668729995275807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating human motion from text has been dominated by denoising motion models either through diffusion or generative masking process. However, these models face great limitations in usability by requiring prior knowledge of the motion length. Conversely, autoregressive motion models address this limitation by adaptively predicting motion endpoints, at the cost of degraded generation quality and editing capabilities. To address these challenges, we propose Bidirectional Autoregressive Motion Model (BAMM), a novel text-to-motion generation framework. BAMM consists of two key components: (1) a motion tokenizer that transforms 3D human motion into discrete tokens in latent space, and (2) a masked self-attention transformer that autoregressively predicts randomly masked tokens via a hybrid attention masking strategy. By unifying generative masked modeling and autoregressive modeling, BAMM captures rich and bidirectional dependencies among motion tokens, while learning the probabilistic mapping from textual inputs to motion outputs with dynamically-adjusted motion sequence length. This feature enables BAMM to simultaneously achieving high-quality motion generation with enhanced usability and built-in motion editability. Extensive experiments on HumanML3D and KIT-ML datasets demonstrate that BAMM surpasses current state-of-the-art methods in both qualitative and quantitative measures. Our project page is available at https://exitudio.github.io/BAMM-page

Related papers

• MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding [76.30210465222218]
  MotionGPT-2 is a unified Large Motion-Language Model (LMLMLM) It supports multimodal control conditions through pre-trained Large Language Models (LLMs) It is highly adaptable to the challenging 3D holistic motion generation task.
  arXiv  Detail & Related papers  (2024-10-29T05:25:34Z)
• Text-driven Human Motion Generation with Motion Masked Diffusion Model [23.637853270123045]
  Text human motion generation is a task that synthesizes human motion sequences conditioned on natural language. Current diffusion model-based approaches have outstanding performance in the diversity and multimodality of generation. We propose Motion Masked Diffusion Model bftext(MMDM), a novel human motion mechanism for diffusion model.
  arXiv  Detail & Related papers  (2024-09-29T12:26:24Z)
• Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs [67.59291068131438]
  Motion-Agent is a conversational framework designed for general human motion generation, editing, and understanding. Motion-Agent employs an open-source pre-trained language model to develop a generative agent, MotionLLM, that bridges the gap between motion and text.
  arXiv  Detail & Related papers  (2024-05-27T09:57:51Z)
• Text-guided 3D Human Motion Generation with Keyframe-based Parallel Skip Transformer [62.29951737214263]
  Existing algorithms directly generate the full sequence which is expensive and prone to errors. We propose KeyMotion, that generates plausible human motion sequences corresponding to input text. We use a Variationalcoder (VAE) with Kullback-Leibler regularization to project the Autoencoder into a latent space. For the reverse diffusion, we propose a novel Parallel Skip Transformer that performs cross-modal attention between the design latents and text condition.
  arXiv  Detail & Related papers  (2024-05-24T11:12:37Z)
• MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generation [19.999239668765885]
  MotionMix is a weakly-supervised diffusion model that leverages both noisy and unannotated motion sequences. Our framework consistently achieves state-of-the-art performances on text-to-motion, action-to-motion, and music-to-dance tasks.
  arXiv  Detail & Related papers  (2024-01-20T04:58:06Z)
• Motion Flow Matching for Human Motion Synthesis and Editing [75.13665467944314]
  We propose emphMotion Flow Matching, a novel generative model for human motion generation featuring efficient sampling and effectiveness in motion editing applications. Our method reduces the sampling complexity from thousand steps in previous diffusion models to just ten steps, while achieving comparable performance in text-to-motion and action-to-motion generation benchmarks.
  arXiv  Detail & Related papers  (2023-12-14T12:57:35Z)
• MMM: Generative Masked Motion Model [10.215003912084944]
  MMM is a novel yet simple motion generation paradigm based on Masked Motion Model. By attending to motion and text tokens in all directions, MMM captures inherent dependency among motion tokens and semantic mapping between motion and text tokens. MMM is two orders of magnitude faster on a single mid-range GPU than editable motion diffusion models.
  arXiv  Detail & Related papers  (2023-12-06T16:35:59Z)
• MoMask: Generative Masked Modeling of 3D Human Motions [25.168781728071046]
  MoMask is a novel framework for text-driven 3D human motion generation. A hierarchical quantization scheme is employed to represent human motion as discrete motion tokens. MoMask outperforms state-of-art methods on the text-to-motion generation task.
  arXiv  Detail & Related papers  (2023-11-29T19:04:10Z)
• DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion [70.33381660741861]
  We present DiverseMotion, a new approach for synthesizing high-quality human motions conditioned on textual descriptions. We show that our DiverseMotion achieves the state-of-the-art motion quality and competitive motion diversity.
  arXiv  Detail & Related papers  (2023-09-04T05:43:48Z)
• Priority-Centric Human Motion Generation in Discrete Latent Space [59.401128190423535]
  We introduce a Priority-Centric Motion Discrete Diffusion Model (M2DM) for text-to-motion generation. M2DM incorporates a global self-attention mechanism and a regularization term to counteract code collapse. We also present a motion discrete diffusion model that employs an innovative noise schedule, determined by the significance of each motion token.
  arXiv  Detail & Related papers  (2023-08-28T10:40:16Z)
• MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model [35.32967411186489]
  MotionDiffuse is a diffusion model-based text-driven motion generation framework. It excels at modeling complicated data distribution and generating vivid motion sequences. It responds to fine-grained instructions on body parts, and arbitrary-length motion synthesis with time-varied text prompts.
  arXiv  Detail & Related papers  (2022-08-31T17:58:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.