# A causal convolutional neural network for multi-subject motion modeling and generation

arXiv: https://arxiv.org/abs/2101.12276 · HTML: https://mlsanity.com/paper/2101.12276/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2021.

## Built on by (most cited first, 8 of 8)

- [Programmable Motion Generation for Open-Set Motion Control Tasks](https://mlsanity.com/paper/2405.19283/) (2024)
- [PoseAnything: Universal Pose-guided Video Generation with Part-aware Temporal Coherence](https://mlsanity.com/paper/2512.13465/) (2025)
- [TopoCap: Learning Topology-Agnostic Motion Priors for Monocular Video-to-Animation](https://mlsanity.com/paper/2606.12153/) (2026)
- [Cycle-World: Mitigating Error Accumulation in Long-term Video World Models via Reverse-Prediction Cycle Consistency](https://mlsanity.com/paper/2607.11836/) (2026)
- [Learning Human Motion from Monocular Videos via Cross-Modal Manifold Alignment](https://mlsanity.com/paper/2404.09499/) (2024)
- [Towards Highly-Constrained Human Motion Generation with Retrieval-Guided Diffusion Noise Optimization](https://mlsanity.com/paper/2605.08054/) (2026)
- [Natural Human Motion Recovery by Aligning High-Order Temporal Dynamics from Monocular Videos](https://mlsanity.com/paper/2605.26879/) (2026)
- [ERF-GS: Reconstructing Fast Motion from Disjoint Event-RGB Viewpoints](https://mlsanity.com/paper/2608.08531/) (2026)

## Abstract (arXiv)

Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of skeleton scale variation on motion style. Moreover, after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset, it is able to synthesize high-quality motions with a personalized style for the novel skeleton. The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks.