TokenMotion: Decoupled Motion Control via Token Disentanglement for Human-centric Video Generation

• URL: http://arxiv.org/abs/2504.08181v1
• Date: Fri, 11 Apr 2025 00:41:25 GMT
• Title: TokenMotion: Decoupled Motion Control via Token Disentanglement for Human-centric Video Generation
• Authors: Ruineng Li, Daitao Xing, Huiming Sun, Yuanzhou Ha, Jinglin Shen, Chiuman Ho,
• Abstract summary: We present TokenMotion, the first DiT-based video diffusion framework that enables fine-grained control over camera motion.<n>Our approach introduces a unified modeling framework utilizing a decouple-and-fuse strategy, bridged by a human-aware dynamic mask.<n>Our work represents a significant advancement in controllable video generation, with particular relevance for creative production applications.
• Score: 7.900728371180723
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-centric motion control in video generation remains a critical challenge, particularly when jointly controlling camera movements and human poses in scenarios like the iconic Grammy Glambot moment. While recent video diffusion models have made significant progress, existing approaches struggle with limited motion representations and inadequate integration of camera and human motion controls. In this work, we present TokenMotion, the first DiT-based video diffusion framework that enables fine-grained control over camera motion, human motion, and their joint interaction. We represent camera trajectories and human poses as spatio-temporal tokens to enable local control granularity. Our approach introduces a unified modeling framework utilizing a decouple-and-fuse strategy, bridged by a human-aware dynamic mask that effectively handles the spatially-and-temporally varying nature of combined motion signals. Through extensive experiments, we demonstrate TokenMotion's effectiveness across both text-to-video and image-to-video paradigms, consistently outperforming current state-of-the-art methods in human-centric motion control tasks. Our work represents a significant advancement in controllable video generation, with particular relevance for creative production applications.

Related papers

• Strong and Controllable 3D Motion Generation [0.0]
  We introduce Motion ControlNet, which enables more precise joint-level control of human motion compared to previous text-to-motion generation methods. These contributions represent a significant advancement for text-to-motion generation, bringing it closer to real-world applications.
  arXiv  Detail & Related papers  (2025-01-30T20:06:30Z)
• Move-in-2D: 2D-Conditioned Human Motion Generation [54.067588636155115]
  We propose Move-in-2D, a novel approach to generate human motion sequences conditioned on a scene image.<n>Our approach accepts both a scene image and text prompt as inputs, producing a motion sequence tailored to the scene.
  arXiv  Detail & Related papers  (2024-12-17T18:58:07Z)
• Motion Prompting: Controlling Video Generation with Motion Trajectories [57.049252242807874]
  We train a video generation model conditioned on sparse or dense video trajectories.<n>We translate high-level user requests into detailed, semi-dense motion prompts.<n>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)
• Image Conductor: Precision Control for Interactive Video Synthesis [90.2353794019393]
  Filmmaking and animation production often require sophisticated techniques for coordinating camera transitions and object movements. Image Conductor is a method for precise control of camera transitions and object movements to generate video assets from a single image.
  arXiv  Detail & Related papers  (2024-06-21T17:55:05Z)
• MotionClone: Training-Free Motion Cloning for Controllable Video Generation [41.621147782128396]
  MotionClone is a training-free framework that enables motion cloning from reference videos to versatile motion-controlled video generation. MotionClone exhibits proficiency in both global camera motion and local object motion, with notable superiority in terms of motion fidelity, textual alignment, and temporal consistency.
  arXiv  Detail & Related papers  (2024-06-08T03:44:25Z)
• MotionMaster: Training-free Camera Motion Transfer For Video Generation [48.706578330771386]
  We propose a novel training-free video motion transfer model, which disentangles camera motions and object motions in source videos. Our model can effectively decouple camera-object motion and apply the decoupled camera motion to a wide range of controllable video generation tasks.
  arXiv  Detail & Related papers  (2024-04-24T10:28:54Z)
• Direct-a-Video: Customized Video Generation with User-Directed Camera Movement and Object Motion [34.404342332033636]
  We introduce Direct-a-Video, a system that allows users to independently specify motions for multiple objects as well as camera's pan and zoom movements. For camera movement, we introduce new temporal cross-attention layers to interpret quantitative camera movement parameters. Both components operate independently, allowing individual or combined control, and can generalize to open-domain scenarios.
  arXiv  Detail & Related papers  (2024-02-05T16:30:57Z)
• Universal Humanoid Motion Representations for Physics-Based Control [71.46142106079292]
  We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. We first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator.
  arXiv  Detail & Related papers  (2023-10-06T20:48:43Z)
• LaMD: Latent Motion Diffusion for Image-Conditional Video Generation [63.34574080016687]
  latent motion diffusion (LaMD) framework consists of a motion-decomposed video autoencoder and a diffusion-based motion generator. LaMD generates high-quality videos on various benchmark datasets, including BAIR, Landscape, NATOPS, MUG and CATER-GEN.
  arXiv  Detail & Related papers  (2023-04-23T10:32:32Z)

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.