Learning to Control Physically-simulated 3D Characters via Generating and Mimicking 2D Motions

• URL: http://arxiv.org/abs/2512.08500v1
• Date: Tue, 09 Dec 2025 11:30:56 GMT
• Title: Learning to Control Physically-simulated 3D Characters via Generating and Mimicking 2D Motions
• Authors: Jianan Li, Xiao Chen, Tao Huang, Tien-Tsin Wong,
• Abstract summary: Mimic2DM is a novel motion imitation framework that learns the control policy directly from 2D keypoint trajectories extracted from videos.<n>We show that the proposed approach is versatile and can effectively learn to synthesize physically plausible and diverse motions across a range of domains.
• Score: 23.080971732537886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video data is more cost-effective than motion capture data for learning 3D character motion controllers, yet synthesizing realistic and diverse behaviors directly from videos remains challenging. Previous approaches typically rely on off-the-shelf motion reconstruction techniques to obtain 3D trajectories for physics-based imitation. These reconstruction methods struggle with generalizability, as they either require 3D training data (potentially scarce) or fail to produce physically plausible poses, hindering their application to challenging scenarios like human-object interaction (HOI) or non-human characters. We tackle this challenge by introducing Mimic2DM, a novel motion imitation framework that learns the control policy directly and solely from widely available 2D keypoint trajectories extracted from videos. By minimizing the reprojection error, we train a general single-view 2D motion tracking policy capable of following arbitrary 2D reference motions in physics simulation, using only 2D motion data. The policy, when trained on diverse 2D motions captured from different or slightly different viewpoints, can further acquire 3D motion tracking capabilities by aggregating multiple views. Moreover, we develop a transformer-based autoregressive 2D motion generator and integrate it into a hierarchical control framework, where the generator produces high-quality 2D reference trajectories to guide the tracking policy. We show that the proposed approach is versatile and can effectively learn to synthesize physically plausible and diverse motions across a range of domains, including dancing, soccer dribbling, and animal movements, without any reliance on explicit 3D motion data. Project Website: https://jiann-li.github.io/mimic2dm/

Related papers

• 3D-Aware Implicit Motion Control for View-Adaptive Human Video Generation [29.389246008057473]
  2D poses rigidly bind motion to the driving viewpoint, precluding novel-view synthesis.<n>3DiMo trains a motion encoder with a pretrained video generator to distill driving frames into compact, view-agnostic motion tokens.<n>Experiments confirm that 3DiMo faithfully reproduces driving motions with flexible, text-driven camera control.
  arXiv  Detail & Related papers  (2026-02-03T17:59:09Z)
• DIMO: Diverse 3D Motion Generation for Arbitrary Objects [57.14954351767432]
  DIMO is a generative approach capable of generating diverse 3D motions for arbitrary objects from a single image.<n>We leverage the rich priors in well-trained video models to extract the common motion patterns.<n>During inference time with learned latent space, we can instantly sample diverse 3D motions in a single-forward pass.
  arXiv  Detail & Related papers  (2025-11-10T18:56:49Z)
• Mocap-2-to-3: Multi-view Lifting for Monocular Motion Recovery with 2D Pretraining [49.223455189395025]
  Mocap-2-to-3 is a novel framework that performs multi-view lifting from monocular input.<n>To leverage abundant 2D data, we decompose complex 3D motion into multi-view syntheses.<n>Our method surpasses state-of-the-art approaches in both camera-space motion realism and world-grounded human positioning.
  arXiv  Detail & Related papers  (2025-03-05T06:32:49Z)
• Motion-2-to-3: Leveraging 2D Motion Data to Boost 3D Motion Generation [43.915871360698546]
  2D human videos offer a vast and accessible source of motion data, covering a wider range of styles and activities.<n>We introduce a novel framework that disentangles local joint motion from global movements, enabling efficient learning of local motion priors from 2D data.<n>Our method efficiently utilizes 2D data, supporting realistic 3D human motion generation and broadening the range of motion types it supports.
  arXiv  Detail & Related papers  (2024-12-17T17:34:52Z)
• 3DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generation [83.98251722144195]
  Previous methods on controllable video generation primarily leverage 2D control signals to manipulate object motions.<n>We introduce 3DTrajMaster, a robust controller that regulates multi-entity dynamics in 3D space.<n>We show that 3DTrajMaster sets a new state-of-the-art in both accuracy and generalization for controlling multi-entity 3D motions.
  arXiv  Detail & Related papers  (2024-12-10T18:55:13Z)
• 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)
• SpatialTracker: Tracking Any 2D Pixels in 3D Space [71.58016288648447]
  We propose to estimate point trajectories in 3D space to mitigate the issues caused by image projection. Our method, named SpatialTracker, lifts 2D pixels to 3D using monocular depth estimators. Tracking in 3D allows us to leverage as-rigid-as-possible (ARAP) constraints while simultaneously learning a rigidity embedding that clusters pixels into different rigid parts.
  arXiv  Detail & Related papers  (2024-04-05T17:59:25Z)
• MotionBERT: A Unified Perspective on Learning Human Motion Representations [46.67364057245364]
  We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources. We propose a pretraining stage in which a motion encoder is trained to recover the underlying 3D motion from noisy partial 2D observations. We implement motion encoder with a Dual-stream Spatio-temporal Transformer (DSTformer) neural network.
  arXiv  Detail & Related papers  (2022-10-12T19:46:25Z)
• MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks [77.56526918859345]
  We present a novel framework that brings the 3D motion task from controlled environments to in-the-wild scenarios. It is capable of body motion from a character in a 2D monocular video to a 3D character without using any motion capture system or 3D reconstruction procedure.
  arXiv  Detail & Related papers  (2021-12-19T07:52:05Z)
• Contact and Human Dynamics from Monocular Video [73.47466545178396]
  Existing deep models predict 2D and 3D kinematic poses from video that are approximately accurate, but contain visible errors. We present a physics-based method for inferring 3D human motion from video sequences that takes initial 2D and 3D pose estimates as input.
  arXiv  Detail & Related papers  (2020-07-22T21:09:11Z)

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.