Summary
LaxMotion removes direct 3D pose supervision and instead learns 3D motion as a structurally consistent explanation of global trajectories and monocular 2D kinematic cues, supported by view, orientation, and stability regularization.
Research paths
How this paper contributes to the site's broader research map.
- Semantic Motion and Embodied InteractionCore work
LaxMotion removes direct 3D pose regression and learns from trajectories, monocular cues, and structural regularization, questioning whether exact labels always improve generalization.
Research question
Exact 3D coordinate supervision can reward reconstruction of fixed training patterns while failing to teach the structural and semantic cues needed to generalize beyond the training distribution.
What the paper contributes
- Reformulates 3D motion generation under relaxed observability without direct 3D pose regression.
- Introduces structured motion factorization using global trajectories and monocular 2D kinematic cues.
- Adds relaxed objectives for view-consistent alignment, orientation coherence, and structural stability.
Evidence and evaluation scope
The paper reports diverse, temporally coherent, and semantically aligned motions with results comparable to or better than fully 3D-supervised methods. Dataset-, protocol-, and metric-specific claims should be taken from the v2 experiments.
Scope and limitations
Relaxed supervision trades exact coordinate targets for assumptions encoded in trajectories, monocular cues, and structural regularizers. Generalization to motion domains with different observability or camera conditions requires separate testing.
Positioning for related work
LaxMotion is a supervision-design contribution for 3D motion generation. It challenges the assumption that more precise 3D labels necessarily produce more generalizable generative models.
Related-work context
Liu et al. propose LaxMotion, a 3D human-motion generation framework that replaces direct 3D pose supervision with global-trajectory and monocular-kinematic constraints plus relaxed structural regularization.
A concise, neutral description of how this paper can be situated in related work.
Official abstract
Recent 3D human motion generation models demonstrate remarkable reconstruction accuracy yet struggle to generalize beyond training distributions. This limitation arises partly from the use of precise 3D supervision, which encourages models to fit fixed coordinate patterns instead of learning the essential 3D structure and motion semantic cues required for robust generalization. To overcome this limitation, we propose LaxMotion, a framework that synthesizes realistic 3D motions without direct 3D pose supervision. Instead of regressing toward exact coordinates, LaxMotion learns 3D motion as a consistent explanation of global trajectories and monocular 2D kinematic cues. We introduce a structured motion factorization together with a reformulated training paradigm under relaxed observability. This design is further supported by relaxed regularization objectives that enforce view consistent alignment, orientation coherence, and structural stability. Under this relaxed supervision paradigm, LaxMotion generates diverse, temporally coherent, and semantically aligned 3D motions, achieving performance comparable to or surpassing fully 3D supervised methods. These results indicate that shifting supervision from exact coordinate matching to structural consistency promotes stronger reasoning and improved generalization, offering a scalable and data efficient paradigm for 3D motion generation.
The abstract is reproduced for scholarly identification and remains under the paper publisher/authors’ original copyright; it is not covered by this page’s CC BY license.
Evidence references
| What to verify | Location in the paper |
|---|---|
| Problem statement | Abstract |
| Method and contributions | Abstract; structured motion factorization and relaxed-regularization sections |
| Evaluation statement | Abstract; comparisons with fully 3D-supervised methods |
Primary source: arXiv record (arXiv:2511.11368v2).
How to cite
Cite the paper—not this explainer—for scientific claims. Cite this page only when reusing its original commentary.
Sheng Liu, Yuanzhi Liang, and Sidan Du. “LaxMotion: Rethinking Supervision Granularity for 3D Human Motion Generation.” European Conference on Computer Vision (ECCV 2026) (2026, forthcoming). arXiv:2511.11368.
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