Mocap-2-to-3: Lifting 2D Diffusion-Based Pretrained Models for 3D Motion Capture

• URL: http://arxiv.org/abs/2503.03222v3
• Date: Sun, 06 Apr 2025 13:54:00 GMT
• Title: Mocap-2-to-3: Lifting 2D Diffusion-Based Pretrained Models for 3D Motion Capture
• Authors: Zhumei Wang, Zechen Hu, Ruoxi Guo, Huaijin Pi, Ziyong Feng, Sida Peng, Xiaowei Zhou,
• Abstract summary: Mocap-2-to-3 is a novel framework that decomposes intricate 3D motions into 2D poses.<n>We leverage 2D data to enhance 3D motion reconstruction in diverse scenarios.<n>We evaluate our model's performance on real-world datasets.
• Score: 31.82852393452607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recovering absolute poses in the world coordinate system from monocular views presents significant challenges. Two primary issues arise in this context. Firstly, existing methods rely on 3D motion data for training, which requires collection in limited environments. Acquiring such 3D labels for new actions in a timely manner is impractical, severely restricting the model's generalization capabilities. In contrast, 2D poses are far more accessible and easier to obtain. Secondly, estimating a person's absolute position in metric space from a single viewpoint is inherently more complex. To address these challenges, we introduce Mocap-2-to-3, a novel framework that decomposes intricate 3D motions into 2D poses, leveraging 2D data to enhance 3D motion reconstruction in diverse scenarios and accurately predict absolute positions in the world coordinate system. We initially pretrain a single-view diffusion model with extensive 2D data, followed by fine-tuning a multi-view diffusion model for view consistency using publicly available 3D data. This strategy facilitates the effective use of large-scale 2D data. Additionally, we propose an innovative human motion representation that decouples local actions from global movements and encodes geometric priors of the ground, ensuring the generative model learns accurate motion priors from 2D data. During inference, this allows for the gradual recovery of global movements, resulting in more plausible positioning. We evaluate our model's performance on real-world datasets, demonstrating superior accuracy in motion and absolute human positioning compared to state-of-the-art methods, along with enhanced generalization and scalability. Our code will be made publicly available.

Related papers

• InteractVLM: 3D Interaction Reasoning from 2D Foundational Models [85.76211596755151]
  We introduce InteractVLM, a novel method to estimate 3D contact points on human bodies and objects from single in-the-wild images. Existing methods rely on 3D contact annotations collected via expensive motion-capture systems or tedious manual labeling. We propose a new task called Semantic Human Contact estimation, where human contact predictions are conditioned explicitly on object semantics.
  arXiv  Detail & Related papers  (2025-04-07T17:59:33Z)
• xMOD: Cross-Modal Distillation for 2D/3D Multi-Object Discovery from 2D motion [4.878192303432336]
  DIOD-3D is the first baseline for multi-object discovery in 3D data using 2D motion. xMOD is a cross-modal training framework that integrates 2D and 3D data while always using 2D motion cues. Our approach yields a substantial performance improvement compared with the 2D object discovery state-of-the-art on all datasets.
  arXiv  Detail & Related papers  (2025-03-19T09:20:35Z)
• Unifying 2D and 3D Vision-Language Understanding [85.84054120018625]
  We introduce UniVLG, a unified architecture for 2D and 3D vision-language learning. UniVLG bridges the gap between existing 2D-centric models and the rich 3D sensory data available in embodied systems.
  arXiv  Detail & Related papers  (2025-03-13T17:56:22Z)
• GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency [50.11520458252128]
  Existing 3D affordance learning methods struggle with generalization and robustness due to limited annotated data.<n>We propose GEAL, a novel framework designed to enhance the generalization and robustness of 3D affordance learning by leveraging large-scale pre-trained 2D models.<n>GEAL consistently outperforms existing methods across seen and novel object categories, as well as corrupted data.
  arXiv  Detail & Related papers  (2024-12-12T17:59:03Z)
• Lifting Motion to the 3D World via 2D Diffusion [19.64801640086107]
  We introduce MVLift, a novel approach to predict global 3D motion using only 2D pose sequences for training.<n> MVLift generalizes across various domains, including human poses, human-object interactions, and animal poses.
  arXiv  Detail & Related papers  (2024-11-27T23:26:56Z)
• UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues [55.69339788566899]
  UPose3D is a novel approach for multi-view 3D human pose estimation. It improves robustness and flexibility without requiring direct 3D annotations.
  arXiv  Detail & Related papers  (2024-04-23T00:18:00Z)
• Realistic Human Motion Generation with Cross-Diffusion Models [30.854425772128568]
  Cross Human Motion Diffusion Model (CrossDiff) Method integrates 3D and 2D information using a shared transformer network within the training of the diffusion model. CrossDiff effectively combines the strengths of both representations to generate more realistic motion sequences.
  arXiv  Detail & Related papers  (2023-12-18T07:44:40Z)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [114.47216525866435]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• Deep Generative Models on 3D Representations: A Survey [81.73385191402419]
  Generative models aim to learn the distribution of observed data by generating new instances. Recently, researchers started to shift focus from 2D to 3D space. representing 3D data poses significantly greater challenges.
  arXiv  Detail & Related papers  (2022-10-27T17:59:50Z)
• Exploring Severe Occlusion: Multi-Person 3D Pose Estimation with Gated Convolution [34.301501457959056]
  We propose a temporal regression network with a gated convolution module to transform 2D joints to 3D. A simple yet effective localization approach is also conducted to transform the normalized pose to the global trajectory. Our proposed method outperforms most state-of-the-art 2D-to-3D pose estimation methods.
  arXiv  Detail & Related papers  (2020-10-31T04:35:24Z)
• Cascaded deep monocular 3D human pose estimation with evolutionary training data [76.3478675752847]
  Deep representation learning has achieved remarkable accuracy for monocular 3D human pose estimation. This paper proposes a novel data augmentation method that is scalable for massive amount of training data. Our method synthesizes unseen 3D human skeletons based on a hierarchical human representation and synthesizings inspired by prior knowledge.
  arXiv  Detail & Related papers  (2020-06-14T03:09:52Z)

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.