SMF: Template-free and Rig-free Animation Transfer using Kinetic Codes

• URL: http://arxiv.org/abs/2504.04831v1
• Date: Mon, 07 Apr 2025 08:42:52 GMT
• Title: SMF: Template-free and Rig-free Animation Transfer using Kinetic Codes
• Authors: Sanjeev Muralikrishnan, Niladri Shekhar Dutt, Niloy J. Mitra,
• Abstract summary: Animation rigs involves applying a sparse motion description to a given character mesh to produce a temporally coherent full-body motion.<n>Existing approaches come with a mix of restrictions - they require annotated training data, assume access to template-based shape priors or artist-designed deformation.<n>We propose Self-supervised Motion Fields (SMF) as a self-supervised framework that can be robustly trained with sparse motion representations.
• Score: 27.44390031735071
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Animation retargeting involves applying a sparse motion description (e.g., 2D/3D keypoint sequences) to a given character mesh to produce a semantically plausible and temporally coherent full-body motion. Existing approaches come with a mix of restrictions - they require annotated training data, assume access to template-based shape priors or artist-designed deformation rigs, suffer from limited generalization to unseen motion and/or shapes, or exhibit motion jitter. We propose Self-supervised Motion Fields (SMF) as a self-supervised framework that can be robustly trained with sparse motion representations, without requiring dataset specific annotations, templates, or rigs. At the heart of our method are Kinetic Codes, a novel autoencoder-based sparse motion encoding, that exposes a semantically rich latent space simplifying large-scale training. Our architecture comprises dedicated spatial and temporal gradient predictors, which are trained end-to-end. The resultant network, regularized by the Kinetic Codes's latent space, has good generalization across shapes and motions. We evaluated our method on unseen motion sampled from AMASS, D4D, Mixamo, and raw monocular video for animation transfer on various characters with varying shapes and topology. We report a new SoTA on the AMASS dataset in the context of generalization to unseen motion. Project webpage at https://motionfields.github.io/

Related papers

• Segment Any Motion in Videos [80.72424676419755]
  We propose a novel approach for moving object segmentation that combines long-range trajectory motion cues with DINO-based semantic features.<n>Our model employs Spatio-Temporal Trajectory Attention and Motion-Semantic Decoupled Embedding to prioritize motion while integrating semantic support.
  arXiv  Detail & Related papers  (2025-03-28T09:34:11Z)
• AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models [5.224806515926022]
  We introduce AnyMoLe, a novel method to generate motion in-between frames for arbitrary characters without external data. Our approach employs a two-stage frame generation process to enhance contextual understanding.
  arXiv  Detail & Related papers  (2025-03-11T13:28:59Z)
• Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamics [67.97235923372035]
  We present Puppet-Master, an interactive video generative model that can serve as a motion prior for part-level dynamics. At test time, given a single image and a sparse set of motion trajectories, Puppet-Master can synthesize a video depicting realistic part-level motion faithful to the given drag interactions.
  arXiv  Detail & Related papers  (2024-08-08T17:59:38Z)
• Temporal Residual Jacobians For Rig-free Motion Transfer [45.640576754352104]
  We introduce Residual Temporal Jacobians as a novel representation to enable data-driven motion transfer. Our approach does not assume access to any rigging or intermediate shapes, produces geometrically and temporally consistent motions, and can be used to transfer long motion sequences.
  arXiv  Detail & Related papers  (2024-07-20T18:29:22Z)
• VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models [58.93124686141781]
  Video Motion Customization (VMC) is a novel one-shot tuning approach crafted to adapt temporal attention layers within video diffusion models. Our approach introduces a novel motion distillation objective using residual vectors between consecutive frames as a motion reference. We validate our method against state-of-the-art video generative models across diverse real-world motions and contexts.
  arXiv  Detail & Related papers  (2023-12-01T06:50:11Z)
• 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)
• Masked Motion Encoding for Self-Supervised Video Representation Learning [84.24773072241945]
  We present Masked Motion MME, a new pre-training paradigm that reconstructs both appearance and motion information to explore temporal clues. Motivated by the fact that human is able to recognize an action by tracking objects' position changes and shape changes, we propose to reconstruct a motion trajectory that represents these two kinds of change in the masked regions. Pre-trained with our MME paradigm, the model is able to anticipate long-term and fine-grained motion details.
  arXiv  Detail & Related papers  (2022-10-12T11:19:55Z)
• MoDi: Unconditional Motion Synthesis from Diverse Data [51.676055380546494]
  We present MoDi, an unconditional generative model that synthesizes diverse motions. Our model is trained in a completely unsupervised setting from a diverse, unstructured and unlabeled motion dataset. We show that despite the lack of any structure in the dataset, the latent space can be semantically clustered.
  arXiv  Detail & Related papers  (2022-06-16T09:06:25Z)
• NeMF: Neural Motion Fields for Kinematic Animation [6.570955948572252]
  We express the vast motion space as a continuous function over time, hence the name Neural Motion Fields (NeMF) We use a neural network to learn this function for miscellaneous sets of motions. We train our model with diverse human motion dataset and quadruped dataset to prove its versatility.
  arXiv  Detail & Related papers  (2022-06-04T05:53:27Z)
• Unsupervised Motion Representation Learning with Capsule Autoencoders [54.81628825371412]
  Motion Capsule Autoencoder (MCAE) models motion in a two-level hierarchy. MCAE is evaluated on a novel Trajectory20 motion dataset and various real-world skeleton-based human action datasets.
  arXiv  Detail & Related papers  (2021-10-01T16:52:03Z)

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.