FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds

• URL: http://arxiv.org/abs/2512.09423v1
• Date: Wed, 10 Dec 2025 08:46:53 GMT
• Title: FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds
• Authors: Marco Pegoraro, Evan Atherton, Bruno Roy, Aliasghar Khani, Arianna Rampini,
• Abstract summary: We introduce FunPhase, a functional periodic autoencoder that learns a phase manifold for motion and replaces discrete temporal decoding with a function-space formulation.<n>FunPhase supports downstream tasks such as super-resolution and partial-body motion completion, generalizes across skeletons and datasets, and unifies motion prediction and generation within a single interpretable manifold.
• Score: 2.6041136107390037
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Learning natural body motion remains challenging due to the strong coupling between spatial geometry and temporal dynamics. Embedding motion in phase manifolds, latent spaces that capture local periodicity, has proven effective for motion prediction; however, existing approaches lack scalability and remain confined to specific settings. We introduce FunPhase, a functional periodic autoencoder that learns a phase manifold for motion and replaces discrete temporal decoding with a function-space formulation, enabling smooth trajectories that can be sampled at arbitrary temporal resolutions. FunPhase supports downstream tasks such as super-resolution and partial-body motion completion, generalizes across skeletons and datasets, and unifies motion prediction and generation within a single interpretable manifold. Our model achieves substantially lower reconstruction error than prior periodic autoencoder baselines while enabling a broader range of applications and performing on par with state-of-the-art motion generation methods.

Related papers

• GeoMotion: Rethinking Motion Segmentation via Latent 4D Geometry [61.24189040578178]
  We propose a fully learning-based approach that directly infers moving objects from latent feature representations via attention mechanisms.<n>Our key insight is to bypass explicit correspondence estimation and instead let the model learn to implicitly disentangle object and camera motion.<n>Our approach achieves state-of-the-art motion segmentation performance with high efficiency.
  arXiv  Detail & Related papers  (2026-02-25T11:36:33Z)
• Towards Arbitrary Motion Completing via Hierarchical Continuous Representation [64.6525112550758]
  We propose a novel parametric activation-induced hierarchical implicit representation framework, called NAME, based on Implicit Representations (INRs)<n>Our method introduces a hierarchical temporal encoding mechanism that extracts features from motion sequences at multiple temporal scales, enabling effective capture of intricate temporal patterns.
  arXiv  Detail & Related papers  (2025-12-24T14:07:04Z)
• Characterizing Motion Encoding in Video Diffusion Timesteps [50.13907856401258]
  We study how motion is encoded in video diffusion timesteps by the trade-off between appearance editing and motion preservation.<n>We identify an early, motion-dominant regime and a later, appearance-dominant regime, yielding an operational motion-appearance boundary in timestep space.
  arXiv  Detail & Related papers  (2025-12-18T21:20:54Z)
• Bidirectional Feature-aligned Motion Transformation for Efficient Dynamic Point Cloud Compression [97.66080040613726]
  We propose a Bidirectional Feature-aligned Motion Transformation (Bi-FMT) framework that implicitly models motion in the feature space.<n>Bi-FMT aligns features across both past and future frames to produce temporally consistent latent representations.<n>We show Bi-FMT surpasses D-DPCC and AdaDPCC in both compression efficiency and runtime.
  arXiv  Detail & Related papers  (2025-09-18T03:51:06Z)
• PatchTraj: Unified Time-Frequency Representation Learning via Dynamic Patches for Trajectory Prediction [14.48846131633279]
  We propose a dynamic patch-based framework that integrates time-frequency joint modeling for trajectory prediction.<n> Specifically, we decompose the trajectory into raw time sequences and frequency components, and employ dynamic patch partitioning to perform multi-scale segmentation.<n>The resulting enhanced embeddings exhibit strong expressive power, enabling accurate predictions even when using a vanilla architecture.
  arXiv  Detail & Related papers  (2025-07-25T09:55:33Z)
• Learning segmentation from point trajectories [79.02153797465326]
  We present a way to train a segmentation network using long-term point trajectories as a supervisory signal to complement optical flow.<n>Our method outperforms the prior art on motion-based segmentation.
  arXiv  Detail & Related papers  (2025-01-21T18:59:53Z)
• Motion-Aware Generative Frame Interpolation [23.380470636851022]
  Flow-based frame methods ensure motion stability through estimated intermediate flow but often introduce severe artifacts in complex motion regions.<n>Recent generative approaches, boosted by large-scale pre-trained video generation models, show promise in handling intricate scenes.<n>We propose Motion-aware Generative frame (MoG) that synergizes intermediate flow guidance with generative capacities to enhance fidelity.
  arXiv  Detail & Related papers  (2025-01-07T11:03:43Z)
• Space-Time Video Super-resolution with Neural Operator [36.715371608285025]
  This paper addresses the task of space-time video super-resolution (ST-MEMVSR) Inspired by recent progress in physics-informed neural networks, we model the challenges of MEMC in ST-VSR. Our approach transforms independent lowresolution representations in coarse-grained continuous function space into refined representations with enriched-temporal details in the fine-grained continuous function space.
  arXiv  Detail & Related papers  (2024-04-09T05:49:04Z)
• FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning [19.491968038335944]
  We introduce a self-supervised, structured representation and generation method that extracts spatial-temporal relationships in periodic or quasi-periodic motions. Our work opens new possibilities for future advancements in general motion representation and learning algorithms.
  arXiv  Detail & Related papers  (2024-02-21T13:59:21Z)
• DiffusionPhase: Motion Diffusion in Frequency Domain [69.811762407278]
  We introduce a learning-based method for generating high-quality human motion sequences from text descriptions. Existing techniques struggle with motion diversity and smooth transitions in generating arbitrary-length motion sequences. We develop a network encoder that converts the motion space into a compact yet expressive parameterized phase space.
  arXiv  Detail & Related papers  (2023-12-07T04:39:22Z)
• Motion In-Betweening with Phase Manifolds [29.673541655825332]
  This paper introduces a novel data-driven motion in-betweening system to reach target poses of characters by making use of phases variables learned by a Periodic Autoencoder. Our approach utilizes a mixture-of-experts neural network model, in which the phases cluster movements in both space and time with different expert weights.
  arXiv  Detail & Related papers  (2023-08-24T12:56:39Z)
• Spatio-Temporal Branching for Motion Prediction using Motion Increments [55.68088298632865]
  Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications. Traditional methods rely on hand-crafted features and machine learning techniques. We propose a noveltemporal-temporal branching network using incremental information for HMP.
  arXiv  Detail & Related papers  (2023-08-02T12:04:28Z)

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.