KinMo: Kinematic-aware Human Motion Understanding and Generation

• URL: http://arxiv.org/abs/2411.15472v1
• Date: Sat, 23 Nov 2024 06:50:11 GMT
• Title: KinMo: Kinematic-aware Human Motion Understanding and Generation
• Authors: Pengfei Zhang, Pinxin Liu, Hyeongwoo Kim, Pablo Garrido, Bindita Chaudhuri,
• Abstract summary: Controlling human motion based on text presents an important challenge in computer vision. Traditional approaches often rely on holistic action descriptions for motion synthesis. We propose a novel motion representation that decomposes motion into distinct body joint group movements.
• Score: 6.962697597686156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Controlling human motion based on text presents an important challenge in computer vision. Traditional approaches often rely on holistic action descriptions for motion synthesis, which struggle to capture subtle movements of local body parts. This limitation restricts the ability to isolate and manipulate specific movements. To address this, we propose a novel motion representation that decomposes motion into distinct body joint group movements and interactions from a kinematic perspective. We design an automatic dataset collection pipeline that enhances the existing text-motion benchmark by incorporating fine-grained local joint-group motion and interaction descriptions. To bridge the gap between text and motion domains, we introduce a hierarchical motion semantics approach that progressively fuses joint-level interaction information into the global action-level semantics for modality alignment. With this hierarchy, we introduce a coarse-to-fine motion synthesis procedure for various generation and editing downstream applications. Our quantitative and qualitative experiments demonstrate that the proposed formulation enhances text-motion retrieval by improving joint-spatial understanding, and enables more precise joint-motion generation and control. Project Page: {\small\url{https://andypinxinliu.github.io/KinMo/}}

Related papers

• MG-MotionLLM: A Unified Framework for Motion Comprehension and Generation across Multiple Granularities [36.42160163142448]
  We pioneer MG-MotionLLM, a unified motion-language model for multi-granular motion comprehension and generation. We introduce a comprehensive multi-granularity training scheme by incorporating a set of novel auxiliary tasks. Our MG-MotionLLM achieves superior performance on classical text-to-motion and motion-to-text tasks.
  arXiv  Detail & Related papers  (2025-04-03T10:53:41Z)
• Motion Prompting: Controlling Video Generation with Motion Trajectories [57.049252242807874]
  We train a video generation model conditioned on sparse or dense video trajectories. We translate high-level user requests into detailed, semi-dense motion prompts. We demonstrate our approach through various applications, including camera and object motion control, "interacting" with an image, motion transfer, and image editing.
  arXiv  Detail & Related papers  (2024-12-03T18:59:56Z)
• MotionBank: A Large-scale Video Motion Benchmark with Disentangled Rule-based Annotations [85.85596165472663]
  We build MotionBank, which comprises 13 video action datasets, 1.24M motion sequences, and 132.9M frames of natural and diverse human motions. Our MotionBank is beneficial for general motion-related tasks of human motion generation, motion in-context generation, and motion understanding.
  arXiv  Detail & Related papers  (2024-10-17T17:31:24Z)
• Autonomous Character-Scene Interaction Synthesis from Text Instruction [45.255215402142596]
  We introduce a framework for synthesizing multi-stage scene-aware interaction motions directly from a single text instruction and goal location. Our approach employs an auto-regressive diffusion model to synthesize the next motion segment, along with an autonomous scheduler predicting the transition for each action stage. We present a comprehensive motion-captured dataset comprising 16 hours of motion sequences in 120 indoor scenes covering 40 types of motions, each annotated with precise language descriptions.
  arXiv  Detail & Related papers  (2024-10-04T06:58:45Z)
• TextIM: Part-aware Interactive Motion Synthesis from Text [25.91739105467082]
  TextIM is a novel framework for synthesizing TEXT-driven human Interactive Motions. Our approach leverages large language models, functioning as a human brain, to identify interacting human body parts. For training and evaluation, we carefully selected and re-labeled interactive motions from HUMANML3D to develop a specialized dataset.
  arXiv  Detail & Related papers  (2024-08-06T17:08:05Z)
• Local Action-Guided Motion Diffusion Model for Text-to-Motion Generation [52.87672306545577]
  Existing motion generation methods primarily focus on the direct synthesis of global motions. We propose the local action-guided motion diffusion model, which facilitates global motion generation by utilizing local actions as fine-grained control signals. Our method provides flexibility in seamlessly combining various local actions and continuous guiding weight adjustment.
  arXiv  Detail & Related papers  (2024-07-15T08:35:00Z)
• Infinite Motion: Extended Motion Generation via Long Text Instructions [51.61117351997808]
  "Infinite Motion" is a novel approach that leverages long text to extended motion generation. Key innovation of our model is its ability to accept arbitrary lengths of text as input. We incorporate the timestamp design for text which allows precise editing of local segments within the generated sequences.
  arXiv  Detail & Related papers  (2024-07-11T12:33:56Z)
• FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis [65.85686550683806]
  This paper reconsiders motion generation and proposes to unify the single and multi-person motion by the conditional motion distribution. Based on our framework, the current single-person motion spatial control method could be seamlessly integrated, achieving precise control of multi-person motion.
  arXiv  Detail & Related papers  (2024-05-24T17:57:57Z)
• Generating Human Interaction Motions in Scenes with Text Control [66.74298145999909]
  We present TeSMo, a method for text-controlled scene-aware motion generation based on denoising diffusion models. Our approach begins with pre-training a scene-agnostic text-to-motion diffusion model. To facilitate training, we embed annotated navigation and interaction motions within scenes.
  arXiv  Detail & Related papers  (2024-04-16T16:04:38Z)
• InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint [67.6297384588837]
  We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs. We demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model.
  arXiv  Detail & Related papers  (2023-11-27T14:32:33Z)
• Act As You Wish: Fine-Grained Control of Motion Diffusion Model with Hierarchical Semantic Graphs [31.244039305932287]
  We propose hierarchical semantic graphs for fine-grained control over motion generation. We disentangle motion descriptions into hierarchical semantic graphs including three levels of motions, actions, and specifics. Our method can continuously refine the generated motion, which may have a far-reaching impact on the community.
  arXiv  Detail & Related papers  (2023-11-02T06:20:23Z)
• SemanticBoost: Elevating Motion Generation with Augmented Textual Cues [73.83255805408126]
  Our framework comprises a Semantic Enhancement module and a Context-Attuned Motion Denoiser (CAMD) The CAMD approach provides an all-encompassing solution for generating high-quality, semantically consistent motion sequences. Our experimental results demonstrate that SemanticBoost, as a diffusion-based method, outperforms auto-regressive-based techniques.
  arXiv  Detail & Related papers  (2023-10-31T09:58:11Z)
• 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)
• MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis [73.52948992990191]
  MoFusion is a new denoising-diffusion-based framework for high-quality conditional human motion synthesis. We present ways to introduce well-known kinematic losses for motion plausibility within the motion diffusion framework. We demonstrate the effectiveness of MoFusion compared to the state of the art on established benchmarks in the literature.
  arXiv  Detail & Related papers  (2022-12-08T18:59:48Z)
• MoDi: Unconditional Motion Synthesis from Diverse Data [51.676055380546494]
  We present MoDi, an unconditional generative model that synthesizes diverse motions. Our model is trained in a completely unsupervised setting from a diverse, unstructured and unlabeled motion dataset. We show that despite the lack of any structure in the dataset, the latent space can be semantically clustered.
  arXiv  Detail & Related papers  (2022-06-16T09:06:25Z)

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.