A two-stage generative model (Graph CVAE + flow matching) learns topology-agnostic motion codes from a new 5k-topology dataset and retargets video motion to arbitrary unseen skeletons.
arXiv preprint arXiv:2501.05098 , year=
12 Pith papers cite this work. Polarity classification is still indexing.
12
Pith papers citing it
abstract
In this paper, we introduce Motion-X++, a large-scale multimodal 3D expressive whole-body human motion dataset. Existing motion datasets predominantly capture body-only poses, lacking facial expressions, hand gestures, and fine-grained pose descriptions, and are typically limited to lab settings with manually labeled text descriptions, thereby restricting their scalability. To address this issue, we develop a scalable annotation pipeline that can automatically capture 3D whole-body human motion and comprehensive textural labels from RGB videos and build the Motion-X dataset comprising 81.1K text-motion pairs. Furthermore, we extend Motion-X into Motion-X++ by improving the annotation pipeline, introducing more data modalities, and scaling up the data quantities. Motion-X++ provides 19.5M 3D whole-body pose annotations covering 120.5K motion sequences from massive scenes, 80.8K RGB videos, 45.3K audios, 19.5M frame-level whole-body pose descriptions, and 120.5K sequence-level semantic labels. Comprehensive experiments validate the accuracy of our annotation pipeline and highlight Motion-X++'s significant benefits for generating expressive, precise, and natural motion with paired multimodal labels supporting several downstream tasks, including text-driven whole-body motion generation,audio-driven motion generation, 3D whole-body human mesh recovery, and 2D whole-body keypoints estimation, etc.
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2026 12roles
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unclear 1representative citing papers
A feed-forward framework predicts unified 3D Gaussian representations of dynamic humans and static scenes from monocular video in a single forward pass.
ICMPG combines LLM-based candidate generation with MPC-style physical simulation and semantic scoring to produce text-driven human motions that are both plausible and faithful.
A data-centric approach shows that less than 3% of AMASS motion data, filtered by physics feasibility, diversity, and complexity, yields better humanoid tracking policies than the full dataset.
AnyMo is a masked-modeling framework for any-modality human motion generation trained on the new OmniHuMo dataset of 5,000+ hours of multimodal motion sequences.
SCRIPT presents a scalable diffusion policy with JAST-DiT architecture, nonlinear history conditioning, and RLHR post-training that claims to outperform prior methods on text alignment, motion quality, and physical realism while scaling on a 1200-hour dataset.
HuM-Eval evaluates human motion videos with a coarse-to-fine approach using VLM global checks plus 2D pose and 3D motion analysis, reaching 58.2% average correlation with human judgments and introducing a 1000-prompt benchmark.
Scaling both synthetic motion data volume and VQ-VAE codebook size yields richer motion vocabularies and measurable gains on standard generation benchmarks.
OMG is a diffusion model for omni-modal whole-body humanoid motion generation that uses language, audio, and reference motions after large-scale data curation to achieve state-of-the-art performance and adaptation.
Humanoid-GPT is a causal Transformer pre-trained on a unified billion-scale motion dataset that tracks dynamic behaviors with zero-shot generalization to unseen motions and tasks.
The paper surveys 3D asset generation methods and organizes them around the full production pipeline to assess which outputs meet engine-level requirements for interactive applications.
LLM model using explicit timestamped skeletal pose sequences and diverse pose/motion supervision achieves SOTA on multiple motion understanding benchmarks, including with 2D input surpassing prior 3D methods.
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GUSH3R: Everyone Everywhere All at Once as Gaussians
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HuM-Eval: A Coarse-to-Fine Framework for Human-Centric Video Evaluation
HuM-Eval evaluates human motion videos with a coarse-to-fine approach using VLM global checks plus 2D pose and 3D motion analysis, reaching 58.2% average correlation with human judgments and introducing a 1000-prompt benchmark.
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Beyond MoCap: Scaling Motion Tokenizers with Synthetic Human Motion for Generative Modeling
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